Conducting cost-benefit analyses using scanner and label data

Conducting cost-benefit analyses using scanner and label data

8

Scanner data and label data can be used to conduct ex ante analyses of the effects of proposed policies and regulations or ex post analyses of the effects of policies and regulations after implementation. In this chapter, we focus on the former as it relates to conducting cost-benefit analysis. Other types of ex ante analyses are based on estimating a demand system (such as in Chapter 6) and conducting simulations of potential changes in taxes, subsidies, or potential restrictions on specific types of foods. For ex post analyses, scanner data are useful for determining whether observed changes in the marketplace are different from secular trends or for comparing treatment to control sites. For example, several recent ex post analyses have used scanner data to evaluate the effects of beverage taxes in local communities (see Section  5.2 for a review of selected studies). The primary use of scanner data for cost-benefit analyses of food policy is for estimating the number of individual products at the barcode level, the number of manufacturers, or the baseline average price and total quantity of a product affected by a particular policy or regulation. Using the variables available within scanner data, researchers can identify specific types of products likely to be affected based on types of product, types of ingredients, nutrient labels, and label claims, or other characteristics; disaggregate estimates by manufacturer size; calculate estimates on a unit, weight, or nutrient basis; and calculate average prices. Scanner data can then be integrated into models that estimate costs or benefits associated with the policy or regulation. (In this case, “models” could refer to simulation models rather than estimation of econometric models.) Scanner data can be used to estimate baseline or compare pre-post changes in food purchases, calories, and nutrients at a very granular product level at an aggregate national level, regional level, or defined market area. However, when analyzing store data or household data for a defined market area, researchers should carefully evaluate the coverage of store scanner data (i.e., types and number of stores in the defined area) and household scanner data (i.e., types and numbers of households in the defined area) to ensure the data are representative of the population of interest (see Section 5.1 for studies evaluating the representativeness of store and household scanner data). This evaluation should take into consideration whether the data are already weighted or can be weighted using projection factors in the datasets, if needed for a complete characterization of the affected products (see Section 4.9), and whether adjustments might need to be made for potential underreporting (see Section 4.3). In this chapter, we describe the general approaches to using scanner data to develop estimates for characterizing affected markets and products as inputs into cost-benefit analysis. We then provide specific examples of a labeling cost model and a reformulation cost model developed for use in analyzing proposed regulations in the United States. Using Scanner Data for Food Policy Research. https://doi.org/10.1016/B978-0-12-814507-4.00008-0 © 2020 Elsevier Inc. All rights reserved.

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8.1 General approach The types of policies or regulations for which barcode-level data are particularly well suited for analyses include those that cause manufacturers or retailers to relabel products, reformulate products, or potentially discontinue production or marketing of a product. In these cases, scanner data are used principally to characterize baseline market conditions and to identify which products might be subject to a specific requirement based on ingredients, nutrients, or labeling. Then, an analysis can estimate impacts on the affected products relative to baseline market conditions. Examples of information needs that can be addressed using scanner data matched to label data include the following: ●

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How many barcoded (or random-weight) products are affected by a policy or regulation? Also, what is the relative distribution of affected branded versus private-label barcodes? How many product formulations are represented in the affected products? That is, how many unique products with the same set of ingredients and processing are there, after accounting for the fact that many products are packaged in multiple package sizes? How many companies produce the affected products, and what is their size distribution (e.g., by categories of annual sales)? What are the nutrient levels in products as a baseline for determining potential changes due to a policy or regulation? Which products contain specific ingredients that might be affected by a policy or regulation? Which products have specific claims on their labels that may be undergoing revision? What are the baseline prices and quantities for use in simulating changes in the marketplace due to a policy or regulation (e.g., using an equilibrium displacement model to estimate effects due to a shift in supply or demand)?

In many cases, store scanner data are easier to use to address these information needs, but analysts may choose to use household scanner data in some instances. Household scanner data have the advantage that private-label barcodes are disaggregated and that they can be used to analyze differences in purchases by demographic category if needed for an analysis (particularly for estimating health benefits associated with a policy). However, household scanner data have known underreporting issues, even when projection factors are applied; therefore, the total number of affected units could be underestimated (see Chapter 4).

8.1.1 Identifying products affected by a policy or regulation The first fundamental step in using scanner data for cost-benefit analysis is to determine which products are affected at a micro level. Then, once the product codes are known, they can be used to conduct the subsequent analyses to estimate costs, benefits, or market responses of a policy or regulation (Box 8.1). Relevant products can be identified using the following: ●

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product category descriptions individual barcode description fields barcode-level descriptor fields such as department, aisle, style, type, and flavor barcode-level label data fields such as nutrients, claims, and ingredients

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Box 8.1  Ensuring barcodes used in analyses are active in the marketplace Scanner data companies often include discontinued barcodes in datasets until those codes are eliminated through periodic (but infrequent) data cleaning procedures. Before reviewing individual barcode descriptions or other fields to determine whether to include the barcodes in an analysis, researchers should ensure that barcodes have positive sales for the year (or other time period) of analysis. In some cases, it may be desirable to exclude barcodes below some threshold of sales to remove potential outliers.

Scanner data companies categorize barcodes into hundreds of product categories or subcategories that are useful for narrowing down the categories that are relevant for an analysis. The product categories broadly define the type of product, such as milk, bread, vegetables, and snack foods but also by key characteristics such as whether products are barcoded or random weight; refrigerated, frozen, or shelf stable; full, low, or nonfat; regular or diet; or flavored or unflavored. The product categories can be used to identify which portions of the data to purchase or prepare for an analysis. The individual barcode description fields refer to text fields that typically include the brand name, the type of product, a short description, and the package size and unit of measure. Additional barcode-level descriptor fields indicate the location of the product in the store (e.g., department or aisle), product-specific characteristics (e.g., style, type, and flavor), and manufacturer or parent company. Because style, type, and flavor are somewhat vaguely defined and definitions vary across product categories, analysts must carefully review these fields to determine how best to use them to narrow down the relevant products for an analysis. When available for an analysis, barcode-level label data can help refine the list of affected products at an even finer level of detail. For example, label data can be used to determine whether products contain a specific nutrient (e.g., calcium) or meet some threshold for a specific nutrient (e.g., greater than 200 mg per serving); whether a product label states a specific type of claim (e.g., calcium content or a claim about the association between calcium consumption and bone health); and, if ingredient data are available, whether a product contains a specific ingredient (e.g., milk). Products can be screened based on the absolute or relative level of a nutrient or whether a specific type of claim is stated on the label. However, because these variables are generally hand entered or coded by the scanner data companies, some degree of error often occurs. In cases where ingredient data are available, the entire list of ingredients is usually included as a long string in one field and thus requires text searches to identify specific ingredients by all variations of the ingredient name.

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8.1.2 Calculating the number of units affected by a policy or regulation Once a researcher has identified the affected barcodes or random-weight codes, scanner data can be used to calculate either the baseline or affected units for a policy or regulation for the selected time period of analysis. Potential measures of units (for affected product category j) include the following: ●

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number of barcodes (or random-weight codes), Xj number of product formulations (i.e., unique recipes), Yj number of sales units, UNITSj number of servings, TOTSRVj total weight of sales units, TOTWGTj total nutrient levels of sales units, TOTNUTj number of manufacturers, TOTMFRj

When estimating the costs of a policy or regulation, the cost per item to reformulate, relabel, or otherwise change the production process can be multiplied by the calculated number of affected items. For example, if each barcoded product, X, must be relabeled, and if it is estimated to cost $1000 to relabel one product, the cost per product times the number of barcoded products equals the total cost of the required change ($1000 • Xj). When estimating the benefits of a policy or regulation, researchers can use purchase volumes as a rough proxy for consumption or the nutrient levels in food purchases for modeling the effects of changes in consumption on health outcomes. For example, if the per capita amount of sugar in purchased sugar-sweetened beverages decreased by 10% from the calculated baseline, a model could be constructed to estimate the reduction in the prevalence of obesity and diet-related diseases associated with the change.

Number of barcodes or random-weight codes (Xj) The number of barcodes or random-weight codes is generally a simple summation of the number of records in the dataset after identifying the relevant products with positive sales volumes. When summing the number of barcodes using store scanner data, private-label products are often aggregated across retailers for confidentiality reasons; therefore, researchers will need to adjust the number of barcodes to approximate the number of codes in the aggregate estimates (see Section 4.5). In addition, products that are usually only sold in specialty stores might not be captured in store scanner data, thus requiring some type of adjustment for undercounting. When using household scanner data, private-label products are disaggregated, and, in theory, the full range of outlets is represented in the data; therefore, product code counts should not require adjustment. However, household scanner data are substantially more costly to acquire and complex to analyze, so researchers may prefer to use store scanner data and develop an approach to adjusting the data.

Number of unique product formulations (Yj) For barcoded products, the number of unique product formulations is less than the number of barcodes because manufacturers sell some products in multiple package sizes (e.g., 4-pack of single-serve versus 32-oz tub of yogurt). The product

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d­ escription fields for products with the same formulations are identical with the exception of the characters that indicate the package size and unit of measure. Therefore, to identify unique product formulations in store scanner data, researchers can remove the package size and unit of measure portion of the product description field, then remove duplicate records from the dataset, and finally sum the number of records to obtain an estimate. In contrast to barcoded products, ­random-weight product categories could comprise multiple product formulations with the exception of single-ingredient foods; thus, there is no direct way to count the number of unique formulations.

Number of units sold (UNITSj) For barcoded and random-weight products, the number of units sold is obtained by summing the data in the units field for the relevant products over the selected time period for analysis. Typically, summing the number of units means adding up the quantities in the units field over 52 weeks of data for the year selected to represent the pre-policy or pre-regulatory baseline. If the units are already weighted in the scanner data to be representative of the geographic area of interest, no further adjustments should be needed. However, if the data are unweighted and weights are not provided by the scanner data company, adjustments will be needed to scale up the number of units to be representative of the population. Researchers could potentially determine scaling factors by comparing total expenditures in the scanner data with government consumer expenditure surveys that provide data at the product category level. For example, as noted in Chapter 4, Sweitzer et al. (2017) reported the results of comparisons between IRI Consumer Network data and government expenditures surveys; results such as these could be used to calculate scaling factors that could be applied to adjust the baseline data for cost-benefit analyses.

Total servings sold (TOTSRVj) For barcoded products, the approach to calculating the number of servings sold varies depending on whether a researcher has access to data from product labels. If product label data are available, the number of servings, SRVij, in each barcoded product i is typically provided as a separate field. In this case, the total number of servings sold in product category j can be calculated directly as: n

TOTSRV j = ∑SRVij • UNITij .

(8.1)

i =1

However, the weight or volume of a serving varies across products; thus, researchers may wish to standardize the weight or volume of a serving size to be consistent across all products. In this case, a researcher would first need to determine the most common weight or volume of a standard serving size for the product category by, for example, determining the median value across the products analyzed. Then, the weight or volume for each product package can be divided by the median weight or volume of a serving to recalculate the number of servings before summing up the total number of servings.

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For barcoded products without label data and for all random-weight products, the number of servings can instead be calculated by dividing the total weight or volume, as described below, by an average serving size for the product category obtained from other sources (i.e., as TOTWGTj/AVGSRVj). For barcoded or random-weight products, an average serving size could be obtained from government data sources, but for barcoded products, an average serving size could also be calculated based on label data recorded from a random sample of products observed in retail stores.

Total weight (or other volume) of product sold (TOTWGTj) To calculate the total weight or other volume of product sold for relevant products, the first step is to ensure that all product weights or volumes, which are derived from the product description field or a product volume field, are expressed in a common unit of measure (see Section 4.2 for a discussion). Then, the total weight or product sold in category j is calculated directly as n

TOTWGT j = ∑WGTij • UNITij .

(8.2)

i =1

Total nutrient quantity in products sold (TOTNUTj) To calculate the total nutrients or components such as calories, saturated fat, or iron in barcoded products sold, levels of nutrients (typically grams or milligrams) are usually obtained from product label data. Nutrient levels are typically expressed on a ­per-serving basis; therefore, the total quantity of nutrient k in products sold in product category j can be calculated as: n

TOTNUT jk = ∑NUTijk • SRVij • UNITij .

(8.3)

i =1

For some products, the package may only contain a single serving, in which case, SRVij, can be dropped from Eq. (8.3). If nutrient levels are not available from label data, researchers could consider calculating an approximate nutrient level using a typical average per-serving nutrient level for the product category. In this case, a researcher would need to collect data on nutrient levels from labels on a random sample of products in stores, calculate the average level, and replace the product-specific NUTijk in Eq. (8.3) with the average level.a For random-weight products, nutrient levels are not available from product labels and therefore must be obtained from alternative sources. The most common sources of nutrient levels in foods are nutrient composition tables that are used for dietary recall studies, such as those used for the US’s National Health and Nutrition Examination Survey; the UK's National Diet and Nutrition Survey; and the Food and Agriculture a

In the United States, an alternative approach is to use the linkage between barcoded or random-weight products and the Food and Nutrient Data for Dietary Studies (FNDDS) codes (see Carlson, Page, Zimmerman, Tornow, & Hermansen, 2019). Then the nutrient levels for the food code in FNDDS can be used as an approximate nutrient level for the product.

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Organization's International Network of Food Data Systems. To use the nutrient composition tables, researchers would need to assign the nutrient levels from the food composition code that most closely correspond to each random-weight product code.

Total number of manufacturers by size (TOTMFRj) For barcoded products, the number of manufacturers for the relevant product category can be calculated by first identifying the number of unique manufacturer names in the company field in the scanner data. If private-label products are aggregated in the data, the number of manufacturers can be approximated based on the number of retailers that are assumed to sell the product (see Section 4.5). Parent companies of the manufacturers are also indicated in scanner data and may be useful depending on the level of company information needed for the analysis. It may also be useful to know the number of different brands produced by manufacturers depending on the focus of the analysis. Brand names of products may be provided as a separate field in the data or can be extracted from the initial characters of the product description field. Note that manufacturers buy and sell their ownership of brands on a relatively frequent basis, and updates in the scanner data may be lagging. Therefore, an important data cleaning step is to ensure that products for each brand are all matched to the same, current manufacturer of the brand. For many types of policy or regulatory analyses, researchers may need to determine impacts based on the size of the manufacturer. In particular, companies of different sizes may respond differently to a policy or regulation, and the costs of reformulating products or changing production processes, if required, may vary by company size. To facilitate analyses by company size, each record in a scanner dataset can be coded with the company size based on different measures of size. In most cases, company size is based on annual sales revenue, but manufacturers can also be classified by size based on number of employees. Researchers can determine the size of product manufacturers based on annual sales in two ways. First, if a researcher has access to comprehensive scanner data that cover all product categories, the total sales revenue over all products can be summed up by manufacturer. Then, manufacturers can be assigned to a size category based on the values that are expected to determine differences in policy responses, costs, or other impacts (e.g., a size categorization could be small manufacturers have less than $10 million in annual sales, medium manufacturers have between $10 million and less than $100 million in annual sales, and large manufacturers have greater than $100 million in annual sales). Then, the number of products (Xj), number of formulas (Yj), number of units sold (UNITSj), or other measures can be calculated by company size. However, when using scanner data to determine sales values at the company level, the assumption is that most of the company sales are represented in scanner data or that sales of barcoded products is the most relevant determinant of differences in impacts. An alternative approach to determining the size of manufacturers in scanner data is to use an external data source such as business registry data (e.g., Dun & Bradstreet data). Using an external data source requires developing programming code to match ­company names, or potentially codes, in the scanner data to the external data source.

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It may be possible to append not only measures of size based on company sales but also number of employees if important for an analysis. Use of an external data source may be particularly beneficial if a researcher is also trying to determine the size of the manufacturer (in this case retailers) for private-label products.

8.1.3 Calculating baseline market prices and quantities Scanner data are particularly useful for calculating baseline market prices and quantities for use in a simulation model to analyze the effects of a policy or regulation.b For example, an equilibrium displacement model could be constructed to estimate changes in market prices, market quantities, and producer and consumer surplus in response to changes in costs (shifting the supply curve) or changes in consumer demand (shifting the demand curve) that could result from a policy or regulation (e.g., as in Wood et al., 2018). The data requirements for parameterizing a simulation model include the average market prices or quantities for the baseline year in addition to own-price and cross-price elasticities of supply and demand and estimates of the shift in the supply and demand curves in response to the policy or regulation. Although it is generally more straightforward to calculate baseline market prices and quantities from store scanner data, household scanner data could also be used. In addition, household scanner data can be useful in determining the demographic characteristics of individuals who purchase the product and thus assist in assessing distributional impacts across the population. To use scanner data for calculating average market prices and quantities, researchers should conduct the following steps: ●

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determine the relevant product categorization and group barcodes in the appropriate categories as described in Section 8.1.1 determine the appropriate measure of quantity, such as pounds or kilograms, using procedures described in Section 8.1.2 and sum over all product codes to calculate the total quantity for the baseline year sum over all product codes to calculate the total sales value for the baseline year divide the total sales value by the total quantity to calculate the baseline average market price

Specifically, the average price, Pj, can be calculated as: Pj =

∑

n

TOTREVij

i =1

∑ i =1Qij n

,

(8.4)

where TOTREVij is the total sales value in dollars, euros, or other denomination over 52 weeks of the year, and Qj could be TOTWGTj or another measure of quantity such as TOTSRVj. Note that by summing the numerator and denominator before dividing, the average price, Pj, is equivalent to a weighted average. In general, units sold, UNITj is not an appropriate measure of quantity because of the wide range of package sizes represented within product categories. b

Note that we do not recommend using average prices calculated from scanner data to assess trends over time because changes in the composition of products and quality of products over time could give misleading results.

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Finally, researchers should consider conducting validity checks of the resulting estimates of prices and quantities. For example, the total quantity measure can be divided by a population estimate to determine if the per-capita purchase quantities appear reasonable relative to what is known about consumption of the product. In addition, the calculated average prices can be compared with government (e.g., US Bureau of Labor Statistics' CPI-Average Price Database) or industry sources (e.g., The Council for Community and Economic Research), if available, to determine if they are somewhat similar or if differences can be explained.

8.2 Examples of ex ante analysis applications We describe two examples using scanner data for regulatory impact analysis in the United States: the US Food and Drug Administration (FDA) Labeling Cost Model and the FDA Reformulation Cost Model, which we constructed using 2012 Nielsen Scantrack data (Muth et al., 2015a, 2015b). Each model can be considered a type of engineering cost model in that costs are calculated using data collected on the labor, materials, and other resources needed to implement each step in the process of changing labels or reformulating foods. Similar to the approach described in Adams, Lee, Piltch, and Jimenez (2019) for assessing food security and nutrition interventions, each model identifies, quantifies, and values the cost associated with each step in the process and then adjusts the costs for inflation. In contrast to the ex ante approach used in the labeling cost and reformulation cost models, econometric estimation of cost functions uses existing data on ex post reported or observed costs of a regulation or policy. These models were used in the regulatory impact analysis of the new Nutrition Facts Label in the United States by FDA (FDA, 2018) and USDA, Food Safety and Inspection Service (USDA, FSIS, 2017). In addition, data from these models have been used in other applications including the regulatory impact analysis for proposed labeling of bioengineered foods in the United States [USDA, Agricultural Marketing Service (AMS), 2018], in a cost-effectiveness analysis of FDA's voluntary sodium reduction goals (Pearson-Stuttard et al., 2018), and in a cost-effectiveness analysis of including added sugars on the Nutrition Facts Label in the United States (Huang et al., 2019). Both models are based on the same set of defined food product categories, but the Reformulation Cost Model also disaggregates by company size because costs of reformulation vary by company size to a much greater extent than costs of relabeling.

8.2.1 Conceptual framework for relabeling or reformulating in response to regulation A company's decisions to relabel or reformulate a product in response to a regulation are linked because a regulation that requires relabeling often leads companies to reformulate products, and likewise, a regulation that causes a company to reformulate products nearly always leads to the need to relabel. For example, if a regulation ­requires

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a nutrient to be listed on a label that companies believe will cause consumers to stop purchasing their product, they may reformulate the product to eliminate or reduce the amount of the nutrient listed on the label. When FDA published a rule requiring the Nutrition Facts Label to include trans fatty acids, food manufacturers reformulated foods to reduce or eliminate trans fatty acids from their products (Hooker & Downs, 2014; Mozaffarian, Jacobson, & Greenstein, 2010; Otite, Jacobson, Dahmubed, & Mozaffarian, 2013; Van Camp, Hooker, & Lin, 2012). Likewise, if a regulation requires manufacturers to replace an ingredient in a product (a type of reformulation), the nutrition information and ingredient list may need to be updated on the label, thus requiring relabeling. Fig. 8.1 provides a simplified overview of the intertwined decisions to relabel and reformulate foods in response to regulation. Companies identify affected products and determine their responses to each regulation. If they determine that only relabeling is needed, the types of changes could be classified as minor, major, or extensive (see Table  8.1), and costs can be estimated using the Labeling Cost Model on a p­ er-barcode, p­ er-formulation, and per-sales unit basis. If they determine that only reformulation is needed, the types of reformulation could include substituting ingredients or changing the production process (see Table 8.2), and costs can be estimated using the Reformulation Cost Model on a per-formulation basis. Each product category in the models includes a count of unique barcodes and formulations for use in calculations. If companies determine that both relabeling and reformulation are needed, total costs can be estimated using both models. However, neither model accounts for the economies of scale that might occur if companies relabel or reformulate multiple products at once or for the fact that companies might decide to discontinue some products or introduce new products in response to the regulation. In other words, the per-unit costs are held fixed over all affected units, and the numbers of barcodes and formulations are held fixed pre- and post-regulation. The product categorization in the Labeling Cost Model and the Reformulation Cost Model were based on the “modules” or product categories in the scanner data as obtained from Nielsen (Box 8.2). To prepare store scanner data for use in the models, we first determined the unit of analysis (barcode, formulations, and sales units) for each type of cost incurred by companies, as shown in Table 8.3. While the Labeling Cost Model includes costs on a per-barcode, per-formulation, and per-unit sold basis, the Relabel products (see Table 8.1)

Regulation requires relabeling Companies identify affected products Regulation requires reformulation

Companies determine response

Cost of relabeling (per barcode, per formulation, per sales unit)

Relabel and reformulate products

Reformulate products (see Table 8.2)

Costs of reformulation (per formulation)

Fig. 8.1  Overview of food relabeling and reformulation in response to regulation.

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Table 8.1  Types of food labeling changes occurring in response to regulation. Type of labeling change

Definition

Minor labeling change

One-color (typically affects black only) changes that do not require a label redesign

Examples ●

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Major labeling change

Multiple-color changes that require a label redesign

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Extensive labeling change

Major format change that requires a change to the product packaging to accommodate labeling information

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Changes to the net quantity statement Minimal changes to a Nutrition Facts Label Minimal changes to an ingredient list Minimal changes to a claim, caution statement, or disclaimer on the back or side of a package Changes to the name of the product Changes to the standard of identity or fanciful name for a food product Addition of a Nutrition Facts Label Substantial changes to an ingredient list Substantial changes to or elimination of a claim Addition of or substantial changes to a caution statement or disclaimer Addition of a peel-back label Increase in the package surface area for labeling information

Source: Derived from Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International.

Reformulation Cost Model only includes per-formulation costs (although conceptually, per-unit sold costs could also be incurred).c We calculated the counts of barcodes, formulations, and sales units by product category using the Nielsen Scantrack scanner data. In addition to the variables shown in Table 8.3, development of the models also required information on other factors that affect costs but are not observed directly in the data. In the case of labeling costs, the primary driver of differences in costs across products is the type of printing method used on the labeling or packaging. For commercially labeled or packaged products, the printing methods are flexography, offset c

Note that various adjustments were made to the data to account for the limitations of the data available from Nielsen at the time. Most of these adjustments are no longer relevant and therefore not described here. More information is available in Muth et al. (2015a, 2015b) if needed.

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Table 8.2  Types of food reformulation occurring in response to regulation. Type of reformulation

Definition

Minor nonfunctional ingredient change

Substitution of an ingredient used at low levels and with limited functional performance Substitution of an ingredient used at low levels with functional or food safety effects

Minor functional ingredient change

Major ingredient change

Production process change

Substitution of a major ingredient used at high levels with functional, food safety, or both types of effects Change in the production process needed to accommodate an ingredient change

Examples ●

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Processing aids Carriers for colors, flavors, and intense sweeteners Anticaking agents Micro-component that is less than 2% by weight of product (based on ingredient labeling requirement) Macro-component that is more than 2% by weight of product

Modification or replacement of processing equipment Reconfiguration of processing line Modification of processing steps (e.g., speeds, durations, flow rates, temperatures)

Source: Derived from Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

lithography (lowest cost), and rotogravure (highest cost), but some manufacturers use digital printing, particularly for small printing runs. For each product category, we first determined the most common method of labeling products (e.g., paper label, paperboard carton, resealable plastic bag). Then, using published data and expert o­ pinion on the distribution of printing methods used for each labeling method, the model calculates a weighted average cost of materials associated with each printing method.

Box 8.2  Classification of barcodes into industry classification codes Barcodes in the Labeling Cost Model and Reformulation Cost Model are also classified using the North American Industry Classification System (NAICS) codes. We mapped each product category into a NAICS code using the NAICS code descriptions provided by the US Census Bureau. Using both classification systems allows researchers to also select product categories and report cost estimates based on NAICS codes if needed for a particular analysis.

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Table 8.3  Level of analysis for relabeling and reformulation costs. Unit of analysis Number of barcodes Branded products Private-label products

Relabeling costs ●

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Number of formulationsa Branded products Private-label products

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Labor and materials costs for administrative activities, graphic design, prepress activities, printing plates, and recordkeeping activities (one time) Analytical testing costs (one time) Market testing costs (one time)

Reformulation costs ●

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Number of units sold Branded products Private-label products

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Discarded inventory and disposal costs for obsolete labels (one time) Increased costs for larger package or peel-back labels to accommodate label information (annual) Increased costs for package inserts if required (annual)

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Not applicable

Reformulation costs (one time) Analytical testing costs (one time) Market testing costs (one time) Although not estimated in the model, potentially, increased costs of alternative ingredients and labor and utilities for altered production process

a

In the Reformulation Cost Model, the number of formulations is further disaggregated by company size (small: less than $1 million in annual sales, medium: $1 to $500 million in annual sales, and large: greater than $500 million in annual sales). The number of companies for private-label products cannot be calculated in scanner data; therefore, reformulation costs for private-label products were assumed to be the same as those for medium-sized companies. Source: Derived from Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International; Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

In the case of reformulation costs, the primary driver of differences in costs is the complexity of formulation. Using the judgment of a set of experts, we assigned a complexity level to each product category (see Table 8.4). Products with higher complexity of reformulation are generally more difficult to reformulate; thus, costs of labor, materials, and other resources are higher. Each food category in the model was then assigned by the experts to one of the three levels so that different levels of costs of reformulation could be attributed to each. Note that complex foods indicated in Table  8.4 have many ingredients, are technologically challenging to formulate, or have interactions among ingredients, whereas simple foods have few ingredients, are technologically straightforward, or are minimally processed (e.g., fresh produce).

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Table 8.4  Food formulation complexity levels for determining costs of reformulation. Low-complexity formulation ●

●

●

●

Any food with a standard of identitya Acidified, shelf-stable, simple food Acid food, shelf-stable, simple Acidified, frozen, simple food

Medium-complexity formulation ●

●

●

●

Low-acid food, shelfstable, simple food Acidified, refrigerated, simple food Acid, refrigerated, simple food Acid, frozen, simple food

High-complexity formulation ●

●

●

Any complex food Low-acid, refrigerated, simple food Low-acid, frozen, simple food

a

In the United States, a food with a standard of identity has federally set requirements for what it must contain in order to be sold in interstate commerce (21 CFR 130-169). Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

8.2.2 Relabeling and reformulation cost model equations and data In describing the equations and data used in the models, we focus on costs incurred on a per-barcode and per-formulation basis. For short compliance periods (e.g., a year or less), food manufacturers may also incur labeling costs on a per-sales unit basis. For example, they may need to apply temporary stickers on products or discard unused labeling or packaging materials. For more details on per-sales unit labeling costs, we refer readers to Muth et al. (2015a). Within a product category, some proportion of products may be unaffected by a regulation because, for example, they do not contain a particular ingredient or nutrient or do not include a particular claim on the label. Each model allows users to determine what percentage of products is assumed to be affected by the regulation. Manufacturers of affected products are assumed to incur the full costs of relabeling or reformulation, while manufacturers of unaffected products are assumed to incur only administrative and recordkeeping costs. Administrative costs include the wages (or opportunity costs of time) associated with the labor hours for reviewing the regulatory requirements and determining whether a response is needed, and recordkeeping costs include the time required to update product records to document the results of the review and determination.

Cost model equations Labeling costs for product category j, LCjL, for labeling change type L (minor, major, or extensive change) are calculated as follows: 4    LC Lj = α j •  AC L + KC L + LC L + ∑β jk • PCkL + OMC L  • x j + ATC L + MTC L • y j  k =1    • (1 + r ) , (8.5)

(

)

Conducting cost-benefit analyses using scanner and label data217

where αj is the estimated proportion of affected products in product category j, ACL are administrative labor costs, KCL are recordkeeping labor costs, LCL are internal labor costs and external consultant costs for graphic design and prepress, βjk is the proportion of products in product category j that uses printing method k, PCkL is the cost of engraving printing plates for printing method k for labeling change type L, OMCL are other materials costs, xj is the number of barcoded products, ATCL is the sum of the costs of analytical tests selected by the model user, MTCL is the sum of the costs of market tests selected by the user, yj, is the number of formulations, and r is a cumulative inflation factor since 2014. ACL, KCL, and the internal labor hours portion of LCL are calculated by multiplying the estimated number of labor hours by the hourly wage rate including benefits and overhead for each type of labeling change. In the case of labeling changes that can be coordinated with a routine change, all but ACj and KCj are dropped from Eq. (8.5), and labor hours are assumed to be half of those for uncoordinated changes. Reformulation costs for product category j, RC jR , for reformulation type change R (minor nonfunctional ingredient substitution, minor ingredient substitution, major ingredient substitution, production process change) are calculated as follows:  3  8   RC jR = α j •  ∑  ∑(Wac • LH acR + UMCacR ) + ATCcR + MTCcR  • y jc  • (1 + r ) , (8.6)   c =1  a =1  where αj is the estimated proportion of affected products in product category j, “c” indexes the three company sizes, “a” indexes the eight activities involved in reformulation, Wac is a weighted average wage rate across the estimated proportion of hours required for each type of employee by company size using wage rates published by the Bureau of Labor Statistics for food manufacturing, LH acR is the estimated number of hours required for each reformulation activity by company size, UMCacR are the utilities and materials costs, ATCcR are analytical testing costs, MTCcR are market testing costs, yjc is the number of formulations, and r is a cumulative inflation factor since 2014. The eight reformulation activities represented in Eq. (8.6) are (1) determination of response to regulation; (2) project management; (3) production reformulation/process modification; (4) packaging assessment; (5) packaging development, if needed; (6) product and package performance testing; (7) production scale-up; and (8) recordkeeping. Unlike in the Labeling Cost Model, the Reformulation Cost Model assumes that reformulation in response to a regulation cannot be coordinated with a routine change. However, the model allows users to enter an assumed percentage of products that manufacturers will decide not to reformulate in response to the regulation (i.e., unaffected products); these products are assumed to incur a few hours of administrative and recordkeeping costs based on company size.

Cost model data Tables 8.5 and 8.6 show examples of the product category data for five breakfast food product categories in the Labeling Cost Model and Reformulation Cost Model that were derived from 2012 Nielsen Scantrack data with adjustments to improve the

566 2199 6331 980

1144 1134 3315 452

1035 903 2019 393

2250

Branded

512 1751 3853 854

1880

Private label

No. of formulas

311.5 330.0 2451.5 343.9

1244.5

Branded

51.4 147.9 411.6 98.5

153.4

Private label

No. of sales units (millions)

Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International.

3216

3326

Breakfast bars/pastries/ powders Breakfasts—frozen Cereal—hot Cereal—ready to eat Waffle/pancake/French toast—frozen

Private label

Branded

Product category

No. of barcodes

Table 8.5  Product category data for breakfast foods in the labeling cost model, 2012.

218 Using Scanner Data for Food Policy Research

No. of barcodes Small company

Medium company

Large company

No. of formulas Small company

Medium company

Large company

Product category

Formulation complexity

Breakfast bars/pastries/ powders Breakfasts— frozen Cereal—hot Cereal— ready to eat Waffle/ pancake/ French toast—frozen

Medium

658

1008

1660

3216

546

703

1001

1880

High

199

585

360

566

184

540

311

512

Medium Medium

242 430

599 1338

293 1547

2199 6331

231 377

482 956

190 686

1751 3853

55

253

144

980

52

221

120

854

High

Branded

Private label

Branded

Private label

Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

Conducting cost-benefit analyses using scanner and label data219

Table 8.6  Product category data for breakfast foods in the reformulation cost model, 2012.

220

Using Scanner Data for Food Policy Research

r­ epresentativeness of the data.d Both models include counts of barcodes and formulas by product category, but the counts for branded products in the Reformulation Cost Model are broken out by company size. The other differences are that the Labeling Cost Model includes counts of sales units for use in calculating costs associated with short compliance periods, and the Reformulation Cost Model indicates the assumed level of formulation complexity. Table 8.7 shows the estimated per-barcode labor hours and costs of relabeling products at the 5th percentile, mean, and 95th percentile for three types of labeling changes that apply to food products. These estimates were derived based on interviews with food manufacturers and packaging and labeling vendors (Muth et al., 2015a). The costs include the wages for employees of the food manufacturer to carry out each step in the label change process, the costs for outside consultants, and the materials costs for printing plates and other materials. Wage rates were obtained from the Bureau of Labor Statistics for manufacturing labor categories in 2014. The differences in costs by printing method are driven by the costs of producing new printing plates for each method. Recall that a minor change affects only one plate, while a major or extensive change affects all printing plates. Per-formulation costs include analytical testing (Table  8.8) and market testing costs (Table  8.9). Analytical testing costs were obtained from the price lists of 16 testing companies, and market testing costs were obtained directly from companies that provide market testing services (Muth et al., 2015a, 2015b). The analytical testing costs assume that two samples per formulation are tested (with the exception of the Nutrition Facts Label developed from a database), 1 h of labor is required to prepare the samples, and the samples are shipped overnight (Muth et al., 2015a, 2015b). The market testing costs were developed assuming the following (Muth et al., 2015a): ●

●

●

●

●

Focus groups—three groups with 8 to 10 consumers each, three products per group, 1.5 h per group Discrimination test—one location with 30 to 100 consumers and one to three products per test Descriptive test—one location with 12 to 100 consumers and three to four products per test Central location test—three to five locations with 100 consumers per location and three to five products per test In-home test—five locations with 100 consumers per location (or distributed across a broader area through direct shipment) and five products per test

When multiple products are included in a test, the costs for the entire test were divided by the number of typical products to determine a per-formula cost for use in the model. Table 8.10 shows the estimated per-formulation costs of reformulating products at the 5th percentile, mean, and 95th percentile by company size, formulation complexity, and type of formulation. The costs shown include labor costs for eight activities conducted by food manufacturers to reformulate products, utilities and materials costs, and default analytical and market testing costs calculated using the prices shown in Tables 8.8 and 8.9 (i.e., the same prices as in the Labeling Cost Model). The estimated labor hours, utilities and materials costs, and default assumptions about the number of analytical and market testing costs were obtained from an expert panel process with seven experts knowledgeable about food product development (see Muth et al., 2015b d

See Muth et al. (2015a) for an explanation of the adjustments.

Minor change 5th P

Mean

Major change 95th P

Extensive change

5th P

Mean

95th P

5th P

Mean

95th P

Label changes that cannot be coordinated with a routine change Labor hours Administrative activities (internal) Graphic design (internal) Prepress activities (internal) Recordkeeping activities (internal) Other costs Consultant labor—graphic design Consultant labor—prepress Materials—flexography Materials—offset Materials—gravure Materials—digital Materials—other

6.1

11.8

17.4

13.9

24.5

35.1

18.6

32.3

44.7

2.1 2.9 1.7

4.6 5.3 2.0

7.3 8.4 2.3

7.2 3.4 1.7

15.3 6.7 2.0

25.2 10.3 2.3

12.3 4.0 1.7

26.7 8.7 2.0

42.4 13.6 2.3

$347

$583

$863

$1044

$1600

$2208

$1632

$3000

$4367

$548 $427 $93 $1082 $0 $51

$900 $833 $143 $1467 $0 $65

$1304 $1283 $214 $1834 $0 $79

$964 $1302 $363 $5047 $0 $154

$1350 $2600 $500 $6333 $0 $195

$1788 $4206 $637 $7792 $0 $236

$1274 $2866 $463 $5401 $0 $257

$1917 $4833 $600 $7500 $0 $325

$2646 $6975 $737 $9859 $0 $393

Label changes that can be coordinated with a routine change Labor hours Administrative activities (internal) Recordkeeping activities (internal)

3.1

5.9

8.7

6.9

12.2

17.5

NA

NA

NA

0.8

1.0

1.2

0.8

1.0

1.2

NA

NA

NA

P, percentile; NA, not applicable because extensive change cannot typically be coordinated with a routine change. Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International.

Conducting cost-benefit analyses using scanner and label data221

Table 8.7  Estimated per-barcode labor hours and materials costs associated with changing food labels, 2014.

222

Using Scanner Data for Food Policy Research

Table 8.8  Analytical testing costs in the labeling and reformulation cost models, 2014 ($/formulation). Type of analytical test Nutrition Facts Label based on laboratory test Nutrition Facts Label based on database Fat composition Trans fatty acids Sugar profile Total fiber Soluble or insoluble fiber Vitamins Vitamin D Minerals Iodine Potassium Sodium chloride pH, brix, Aw (water activity) Proximate analysis Pathogens Caffeine Acrylamide Allergens Bioengineered ingredients

5th percentile

Mean

95th percentile

742

845

968

111

188

262

122 122 87 132 155

168 172 104 194 212

208 225 122 265 272

93 179 23 55 22 19 13

169 243 42 107 40 29 20

257 309 64 170 63 37 27

66 34 74 216 85 147

108 77 102 227 125 276

161 126 128 239 175 414

Note: The total test costs included in the model assume two tests are conducted, 1 h of labor is required to prepare the samples, and samples are shipped overnight to the testing lab. Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International; Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

for more details). Wage rates were obtained from the Bureau of Labor Statistics for manufacturing labor categories in 2014.

8.2.3 Estimated relabeling and reformulation costs for a hypothetical regulation affecting breakfast cereals To demonstrate the outputs of the model, we assumed a hypothetical regulation will cause breakfast cereal manufacturers (hot cereals and ready-to-eat cereals) to substitute a major ingredient in product formulations. Recall that a major ingredient is one that comprises more than 2% of the product volume (see Table 8.2). This change in

Conducting cost-benefit analyses using scanner and label data223

Table 8.9  Estimated market testing costs in the labeling and reformulation cost models, 2014 ($/formulation). Type of market test

5th percentile

Mean

95th percentile

Focus groups Discrimination test Descriptive test Central location test In-home test

6158 4973 8594 24,733 21,776

6500 6300 13,058 31,950 27,350

6842 7784 16,534 39,162 32,922

Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., & Karns, S. A. (2015a). 2014 FDA labeling cost model. Research Triangle Park, NC: RTI International; Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

the ingredients would also cause manufacturers to relabel their products to update the Nutrition Facts Labels, ingredient lists, or other label information. To estimate industry costs associated with the hypothetical regulation, we selected a “major” labeling change in the Labeling Cost Model, and “substitution of a major ingredient” in the Reformulation Cost Model. In addition, we selected the following: ●

●

●

●

●

●

24-month compliance periode 35% of products are affected in both models (i.e., 35% of products contained an ingredient that needs to be substituted) changes to labels on affected products cannot be coordinated with a routine labeling change because relabeling is being driven by the requirement to reformulate included default analytical and consumer tests in the Reformulation Cost Model and no additional test costs in the Labeling Cost Model to avoid double-counting included recordkeeping costs in the Reformulation Cost model and no additional recordkeeping costs in the Labeling Cost Model to avoid double-counting a cumulative inflation factor of 6% from 2014 (baseline year of the models) to 2018 based on the consumer price index

The Labeling Cost Model assumes hot cereals will be labeled with a paper label and ready-to-eat cereals will be labeled with a paperboard carton based on the ­top-selling products in the scanner data. Based on the percentage distribution of products by printing method from Muth et al. (2015a), 40% of paper labels are assumed to be printed using flexography, 50% by offset lithography, 5% by rotogravure, and 5% digitally. In comparison, 20% of paperboard cartons are assumed to be printed using flexography, 78% by offset lithography, 2% by rotogravure, and none digitally. The estimated costs of the hypothetical regulatory scenario are shown in Table 8.11 (labeling costs) and Table 8.12 (reformulation costs). A total of 4543 hot cereal and ready-to-eat cereal products would be affected by the regulation for a total industry cost of relabeling of $39.9 million (ranging from $20.4 million to $70.6 million) and e

A 24-month compliance period means that only per-barcode and per-formulation costs are included (i.e., no per-unit costs) in the Labeling Cost Model and that only large companies would incur additional labor costs due to the rapid response time in the reformulation cost model.

Small companies Type of reformulation

5th percentile

Mean

Medium companies

95th percentile

5th percentile

Mean

224

Table 8.10  Total per-formula costs by company size, formulation complexity, and type of reformulation, 2014. Large companies

95th percentile

5th percentile

Mean

95th percentile

Low complexity food $2269

$5215

$9890

$26,920

$60,127

$112,049

$64,449

$141,614

$260,491

$9795

$18,638

$32,199

$107,590

$232,164

$424,692

$258,853

$554,375

$1,000,701

$16,540

$31,526

$55,679

$376,951

$794,935

$1,436,930

$1,040,817

$2,068,618

$3,611,619

$31,192

$60,488

$108,924

$596,484

$1,270,314

$2,307,509

$1,562,662

$3,181,138

$5,629,886

$2910

$6750

$12,850

$28,774

$64,243

$119,665

$68,958

$151,519

$278,708

$11,110

$21,709

$38,016

$121,082

$263,094

$482,836

$289,691

$623,183

$1,128,213

Medium complexity food Substitution of a minor nonfunctional ingredient Substitution of a minor functional ingredient

Using Scanner Data for Food Policy Research

Substitution of a minor nonfunctional ingredient Substitution of a minor functional ingredient Substitution of a major ingredient Change in production process (and ingredient change)

$19,987

$38,668

$69,754

$467,068

$992,184

$1799, 815

$1,236,494

$2,509,306

$4,430,231

$42,732

$81,585

$147,850

$790,549

$1,684,419

$3,061,436

$1,940,254

$4,053,482

$7,267,054

$4678

$10,796

$20,437

$28,774

$64,243

$119,665

$68,958

$151,519

$278,708

$14,600

$29,800

$53,357

$121,082

$263,094

$482,836

$289,691

$623,183

$1,128,213

$27,027

$54,851

$100,203

$481,950

$1,028,010

$1,868,913

$1,287,689

$2,604,100

$4,591,038

$65,018

$129,801

$236,362

$826,368

$1,767,827

$3,220,168

$2,094,956

$4,312,567

$7,680,790

High complexity food Substitution of a minor nonfunctional ingredient Substitution of a minor functional ingredient Substitution of a major ingredient Change in production process (and ingredient change)

Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

Conducting cost-benefit analyses using scanner and label data225

Substitution of a major ingredient Change in production process (and ingredient change)

226

Table 8.11  Estimated relabeling costs of a hypothetical regulation affecting 35% of breakfast cereals (2018$). Product category Cereal—hot

Cereal—ready to eat

Total labeling costs (millions $s)

Brand type

No. of formulas

5th P

Mean

95th P

5th P

Mean

95th P

Branded Private label Subtotal Branded Private label Subtotal

397 770 1167 1160 2216 3376

316 613 929 707 1349 2055

4372 6511 5783 4043 4043 4043

8740 11,592 10,622 8136 8136 8136

15,636 19,201 17,988 14,685 14,685 14,685

1.7 5.0 6.7 4.7 9.0 13.7

3.5 8.9 12.4 9.4 18.0 27.5

6.2 14.8 21.0 17.0 32.5 49.6

4543

2984

4490

8775

15,533

20.4

39.9

70.6

P = percentile. Source: Muth, M. K., Bradley, S. R., Brophy, J. E., Capogrossi, K. L., Coglaiti, M. C., Karns, S. A., & Viator, C. L. (2015b). 2014 FDA reformulation cost model. Research Triangle Park, NC: RTI International.

Using Scanner Data for Food Policy Research

Total

Labeling cost per barcode ($s)

No. of barcodes

Table 8.12  Estimated reformulation costs of a hypothetical regulation affecting 35% of breakfast cereals (2018$). Total reformulation costs (million $s) Product category

Brand type

No. of barcodes

No. of formulas

5th percentile

Mean

95th percentile

Small companies Cereal—hot

Cereal— ready to eat

Branded Private label Subtotal Branded Private label Subtotal

Total

85 0

81 0

1.7 –

3.3 –

6.0 –

85 151 0

81 132 0

1.7 2.8 –

3.3 5.4 –

6.0 9.8 –

151

132

2.8

5.4

9.8

235

213

4.5

8.7

15.7

Medium companies Cereal—hot

Cereal— ready to eat

Branded Private label Subtotal Branded Private label Subtotal

Total

210 770

169 613

83.5 303.5

177.4 644.6

321.8 1169.2

979 468 2216

782 335 1349

387.0 165.7 667.8

822.0 351.9 1418.3

1491.0 638.3 2572.7

2684

1683

833.4

1770.2

3211.1

3663

2465

1220.5

2592.2

4702.1

Large companies Cereal—hot

Cereal— ready to eat

Branded Private label Subtotal Branded Private label Subtotal

Total

103 0

67 0

130.7 –

265.3 –

468.4 –

103 541 0

67 240 0

130.7 472.0 –

265.3 957.9 –

468.4 1691.2 –

541

240

472.0

957.9

1691.2

644

307

602.7

1223.3

2159.7

All companies Cereal—hot

Cereal— ready to eat

Total

Branded Private label Subtotal Branded Private label Subtotal

397 770

316 613

216.0 303.5

446.1 644.6

796.2 1169.2

1167 1160 2216

929 707 1349

519.4 640.5 667.8

1090.6 1315.3 1418.3

1965.4 2339.3 2572.7

3376

2055

1308.3

2733.6

4912.1

4543

2984

1827.7

3824.2

6877.5

228

Using Scanner Data for Food Policy Research

a total industry cost of reformulation of $3.8 billion (ranging from $1.8 billion to $6.9 billion). Thus, reformulation costs vastly exceed relabeling costs, although in this example all the costs of analytical testing and consumer testing were included with the reformulation costs. Costs of relabeling are not broken out by company size because costs are assumed to be similar across sizes, but costs of reformulation are assumed to differ by company size. The largest portion of industry costs of reformulation falls on medium-size companies because they produce the largest number of products, but their per-formula costs (as shown in Table 8.10) are less than per-formula costs for large companies. Furthermore, all private-label products are assigned to the medium-size category under the assumption that the retailers that produce private-label products would incur reformulation costs similar to medium-size companies. Although this example is hypothetical, it provides a general sense of the drivers of relabeling and reformulation costs and the relative magnitudes of costs.

8.3 Concluding remarks In a cost-benefit analysis, the total costs of relabeling and reformulation as calculated above can be compared with the predicted health benefits associated with the regulation to determine whether the value of the health benefits exceeds the costs. Health benefits could arise from changes in the product, either because it provides additional nutritional benefits or avoids a potential risk, or from changes in consumer purchasing and consumption patterns in response to the changes in the information on the label. When comparing costs versus benefits, most of the costs of relabeling and reformulation are one-time costs, but some costs could increase annual costs of production (e.g., higher costs of ingredients). In contrast, most of the health benefits occur on an ongoing basis. Therefore, researchers must determine an appropriate time horizon when comparing the costs and benefits. Finally, if food manufacturers pass along the increased costs of production associated with a regulation, market prices will increase, thus reducing the quantity of product purchased by consumers. Researchers could use the estimated relabeling and reformulation costs in a partial-equilibrium model to assess the potential market effects of a regulation. These market effects could reflect a shift in the supply curve due to increased costs of production and a shift in the demand curve due to consumer response to changes in information on the label or product attributes. The baseline market prices and quantities for a partial-equilibrium model could also be calculated using scanner data as described in Section 8.1.3.

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