State Level Food System Indicatorsa

Robert P. King, Molly Anderson, Gigi DiGiacomo, David Mulla and David Wallingab

August 2012

(Updated August 2016)

a The State Level Food System Indicators Project was funded by the Healthy Foods, Healthy Lives Institute, University of Minnesota. b Robert P. King is a professor in the Department of Applied Economics at the University of Minnesota. Molly Anderson is the William R. Kenan Jr. Professor of Food Studies at Middlebury College. Gigi DiGiacomo is a research fellow in the Department of Applied Economics. David Mulla is the W.E. Larson Chair and Professor in the Department of Soil, Water and Climate at the University of Minnesota. David Wallinga is Senior Health Officer, Health Program at the Natural Resources Defense Council. The authors wish to acknowledge Mary Story, Professor in the School of Public Health at the University of Minnesota, who served on the project team during the conceptual phase of the project; Joel Nelson, Department of Soil, Water and Climate at the University of Minnesota, who provided leadership and expertise in the design of the indicator maps; and Lisa Jore, from The Food Industry Center at the University of Minnesota, who directed web site design and development. We also thank undergraduate students Megan Dehn, Gustavus Hulin, Andrew McBride, Luke Natkze, Ryan Orton, Kyle Swenson, and Lumei Zheng, who assisted with data compilation, map and state fact sheet generation, and web site development. Finally, we thank graduate student Ben Scharadin, who conducted the principal component analysis described in Appendix 3.

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State Level Food System Indicators

Executive Summary Researchers, policy makers and the stakeholders they serve need comprehensive food system indicators that can be readily accessed, updated and compared across locations and over time. In this study we develop a set of state level food system indicators and compile data on them for all 50 states for the period 1997 – 2007. While our work builds upon insights from previous food system indicator studies, it differs from past efforts in that we collect data at regular time intervals for multiple states at a consistent level of geopolitical resolution. From an initial suite of more than 200 indicators, 63 unique indicators were selected using criteria such as continuity and consistency, accessibility and geographic scope. These indicators measure structural, economic, environmental, health and social changes in the food system. Data for each of the 63 indicators were compiled primarily from government sources such as the U.S. Census Bureau, the U.S. Department of Agriculture (USDA), the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Centers for Disease Control. The two largest sources of data employed in our study are the U.S. Census Bureau’s Economic Census and the USDA’s Census of Agriculture. Two indicator summary tools - national maps and state fact sheets - were developed and first released in 2012 to communicate indicator data and to highlight differences across states and changes over time. Approximately 300 maps, color coded by performance measure, and 153 state and national fact sheets were generated for the project and are provided online, along with their data sources, to encourage hypothesis generation and testing as well as community goal setting and monitoring. Since 2012, the indicator tools have been used by students at various institutions, referenced by the National Academy of Sciences,1 and utilized by researchers assessing regional food systems.2 The indicator fact sheets and maps have been updated with 2012 census data and are available, along with original sample presentations based on the indicator data and the spreadsheet containing the data compiled for the project, at: http://www.hfhl.umn.edu/research/food-system-indicators.

1 “A Framework for Assessing Effects of the Food System.” National Academy of Sciences. 2015. 2 Eshleman, John and Kate Clancy, “The Northeast Food System: Context for Innovative Research.” Enhancing Food Security in the Northeast. February 9, 2015. Kleinschmidt, Jim, Emily Barker, Dr. Dennis Keeney, Robin Major, Mary Huebert, and Julia Olmstead. “Measuring Success: Local Food System and the Need for New Indicators.” Institute for Agriculture and Trade Policy. Accessed 5.31.16. www.iatp.org/files/indicators-web.pdf .

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1. Introduction The food system is complex and multifaceted. It affects human health, the environment, and the economy. It is also closely linked to culture and our sense of community. Knowledge of the current status of the food system and of the linkages between policy initiatives and food system performance is a precondition for sound food policy. Food policy is recognized to be inextricably linked to important dimensions of economic, social, and environmental policy. Following enactment of the 2008 Farm Bill, American Farmland Trust (http://162.242.222.244/programs/farm-bill/debate/influences.asp) observed that “new forward-looking alliances” had formed during policy debate and that these alliances “have permanently changed the political landscape for farm and food policy … The as the individual interests gained a better understanding of common leverage points and aligned agendas.” Community planners at the state and sub-state levels also recognize the importance of food systems issues for their work. In its 2007 Policy Guide on Community and Regional Food Planning, the American Planning Association adopted a series of recommendations “to strengthen connections between traditional planning and the emerging field of community and regional food planning” noting that food system activities have significant impacts on the availability of urban and rural land; the amount of fossil fuel energy consumed within a region (to produce, process, transport, and dispose of food); the incidence of hunger and food nutrition within low-income communities; and the pollution of ground water and surface water resulting from some agricultural practices. Policy makers and the stakeholders they serve need comprehensive indicators that can be readily accessed and updated and that can be compared across locations and over time (Sachs et al. 2010). Efforts to develop comprehensive sets of indicators that describe and assess the status of the food system have been proliferating in recent years. Often the focus for these indicator sets is sustainability. As tools for monitoring and assessment, indicators can be used to characterize the current state of the food system and to measure changes in its state over time. The U.S. National Research Council (NRC) defines sustainability indicators as “… repeated observations of natural and social phenomena that represent systematic feedback. They generally provide quantitative measures of the economy, human well-being, and impacts of human activities on the natural world.” (NRC 1999, 233-234) Food system indicators are measured at different scales: e.g., a city, county, state, region, or country. Two noteworthy examples of indicator systems for assessment and monitoring that focus on national indicators are: (1) that developed under the Wallace Center’s “Charting Growth to Good Food” project, which identified indicators for measuring the availability of “good food” in the United States (Anderson 2009) and (2) the recent effort by the UK’s Department for Environment, Food, and Rural Affairs (DEFRA n.d.) to develop and implement a framework for assessing food system performance. The Vivid Picture Project in California (Hildebrand 2004; Feenstra 2005) and Cultivating Resilience Project in Iowa (Tagtow and Roberts 2011) are examples of indicator studies focusing on the food system in a single state. A series of county-level studies conducted by University of California – Davis researchers serve as excellent examples of indicator systems focused on much smaller communities (King and Feenstra 2001; Cozad et al. 2002; Anderson et al. 2002). Finally, the U.S. Department of Agriculture-Economic Research Service Food Environment Atlas (http://www.ers.usda.gov/FoodAtlas/) is a rich food system indicator database that provides access to data on a wide range of food system indicators related to food choices, health and well-being, and community characteristics with varying levels of spatial resolution.

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In this study we develop a set of state level food system indicators and compile data on them for all 50 states for the period 1997 – 2007. While our work builds upon insights from previous food system indicator studies, it differs from past efforts in that we use data collected at regular time intervals for multiple states at a consistent level of geopolitical resolution. This facilitates comparisons over space and time. It also makes it possible to explore the feasibility of using data reduction techniques such as principal components analysis to construct composite indicators and to assess the validity and stability of the conceptual framework underlying the indicator system. We define the food system as an interconnected set of biological, technological, economic, and social activities and processes that nourish human populations and provide livelihood and satisfaction to the people who participate in it. The food system encompasses activities that extend from the provision of inputs for primary food production through farming, food processing and manufacturing, food distribution and retailing, food consumption, and post-consumption food waste. It extends across community, state, and national borders, but it can also be described and evaluated at a specific level of spatial resolution, such as a state. The indicators selected for this study are both descriptive and evaluative. As descriptive measures, they help users understand how the food system differs across states or regions, as well as how it is changing over time in a single location. As evaluative measures, they help users assess how food system performance meets or fails to meet certain goals. Descriptive indicators characterize the structure, size, and diversity of participants for each of the activities across the food system. Evaluative indicators include measures of performance and impacts along economic, environmental, health, and social dimensions. In the remainder of this report, we first provide a review of the literature on the conceptual foundations for indicator systems and past food system indicator project. We then present the conceptual framework for our food system indicators and describe the processes for identifying a “wish list” of indicators and for narrowing down that list to a final set of indicators. Next, we describe the data compilation process and key sources for the data we used. In the closing sections we present sample findings derived from the indicators and describe how two summary tools from this study – state fact sheets and food system indicator maps – can be used to describe the food system and evaluate food system performance and outcomes. Lastly, we identify issues for future research. This report also includes three appendices. The first appendix, Appendix A: Indicators, presents a comprehensive list of indicators identified for this project. The second, Appendix B: Indicator Definitions, Sources and Compilation Methods, provides metadata (detailed definitions and information on data sources for each of the final set of selected indicators). The third appendix, Appendix C: Principal Components Analysis of State Level Food System Indicators, presents methods and key findings from an effort to use principal components analysis reduce the size and complexity of the indicator set by creating composite indicators. Findings from this aspect of the study have important implications for future work on indicator systems.

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2. Background The emphasis of this indicators project is on state level indicators that allow comparisons across all states in the United States and across multiple food system activities, from inputs through waste, and multiple performance areas—economic, environmental, health and social. Therefore, this review focuses on national-scale indicators that extend across the entire food system. Most previous research and indicator development efforts have focused on factors affecting the amount and efficiency of agricultural production, from on-farm to national scales. Over the last few decades, the emphases of indicator projects have changed with a) growing realization that agriculture is multifunctional and affects many performance areas; b) growing realization that agricultural production is part of a food system that also comprises activities such as input supply, distribution, sales avenues and waste management; and c) changes in the structure of value chains so that vertically integrated companies control a large proportion of the food system. This project reflects the first two of these changes, by providing indicators across the entire food system and showing the food system’s combined impacts. Growing concerns about the sustainability of human activities have contributed to the evolution of agricultural indicators. The understanding of activities related to agriculture as systemic, complex and interconnected with multiple other domains comes from the merger of concerns about sustainability and agriculture. In the United States, “sustainable agriculture” as a public goal entered the literature and public discourse in the 1980s, first with attention to environmental effects of agriculture and later with attention to social and cultural effects. Since then, the recognition has grown that agriculture must be part of any strategy to increase human sustainability because of its significant influences on the environment and society, and the ways that environmental and social conditions affect agriculture. Understanding these impacts fully requires looking at the entire food system and not just production activities. This review first summarizes the key points about indicator development and use from the literature, and then describes previous efforts to develop indicators that are applicable at a national level. We acknowledge that considerable work has gone into developing indicators at other scales (farm-level, company-level, city or region). This work has fed into national food-system indicators; usually national-scale indicators rely on data at a finer grain, such as county or state. However, we did not include indicator projects that focused on application to a single farm, city or region in this review. The literature review concludes with a section on recent trends in indicator development and use, which reflects the third change in emphasis above (due to structural changes in food systems). Indicator Development and Use – Key Points

The term “indicator” means different things in different contexts, ranging from a data point to a goal. Several authors (e.g., Rao and Rogers 2006; Russillo and Pintér 2011) have noted the need to come to agreement on the definitions of terms such as indicator, trend, target, driving force, and criterion. Parris and Kates (2003, pp. 572-3) provided the following definitions of commonly used terms:

Goals are broad statements about objectives; indicators are quantitative measures selected to assess progress toward or away from a stated goal; targets use indicators to make goals specific with endpoints and timetables; trends are changes in the values of indicators over time; driving forces and policy responses are processes that influence trends and our ability to meet targets.

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Gallopín (1997) provides a similar definition of an indicator as summarizing information about a particular phenomenon, pointing out that indicators are variables rather than values. The relevance of the information that an indicator provides depends on the user’s needs, vantage point and interpretation (Birkmann 2006). For the purposes of this review, the term “indicator” will refer to information about the perceived state of a complex system, selected to reflect values about what is important to monitor in the system (Meadows 1998). Meadows’s use is congruent with how we used the term in this project. She states, “Not only do we measure what we value, we come to value what we measure” (1998, pg. 2). This reinforces the importance of choosing good indicators, and choosing them well. Multiple guides to developing indicators recommend how to choose good indicators well. Indicators can be descriptive (as we used them) or normative (tied into explicit goals to improve a system); guidelines for different purposes are somewhat different. General points include: Define the purposes of the indicators and their intended audience early on. Aimee Russillo and Laszló Pintér, two experts on indicator development, stress the fundamental importance of clarity about why indicators are being developed in their excellent review paper on linkages between farm-level measurement systems and environmental sustainability (Russillo and Pintér 2011, pg. 14). Drawing on their experience, they claim:

Most metric initiatives begin with a general framework of sustainability along social, environmental and economic themes. Participants generate exhaustive lists of indicators based on existing criteria and indicator systems. Then, in a series of workshops and conference calls, they pare down these lists to a smaller set, which is then divided up or assigned to expert groups to define. The interdependence of the issues and the focus on priority key impacts and risk areas gets diluted.

One of the common pitfalls of indicator development is a tendency to include too much data, rather than set priorities among the data available or run the risk of leaving out something that is important to a potential user (Russillo and Pintér 2011). A clear understanding of the purpose and audience for indicators from the outset of a project can help to avoid this problem. Our project’s purposes were to describe, compare, assess and teach about the food system in different states. By making data available in a comparable form across all states and three points in time, we provided a teaching tool (state factsheets) that could serve many users. By making the database available online, users can tailor results to their specific purposes. Agree on a conceptual framework and common principles. The most common conceptual framework for agricultural indicators is some variant of the pressure-state-response model, such as the Driving Forces/ Pressures/ States/ Impacts/ Responses or DPSIR model used by the European Environment Agency and UN agencies, and socioeconomic and environmental themes. Currently, there is no agreed-upon international conceptual framework for food system assessment, although FAO and the ISEAL Alliance (a global association of sustainability standards) recently proposed one based on two years of consultation and research (Guttenstein et al. 2010; FAO 2012). Common principles for sustainable indicator development were developed from a meeting of international measurement practitioners and researchers in Bellagio, Italy, in November, 1996, to review international progress on indicators of sustainable development and synthesize practical insights from ongoing efforts. These Bellagio

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Principles emphasize accessibility, transparency, use of key indicators and standardized measurement methods, communication that meets the needs of stakeholders, broad participation, a coherent framework and goals, an assessment process to allow learning, and sufficient institutional capacity for indicator development.3 Use a holistic approach and aids to common understanding. Understanding agricultural and food system impacts in multiple areas requires a holistic, interdisciplinary approach; and graphic mental models can help create a common understanding of the system (Risku-Norja and Mäenpääb 2007; Aubrun et al. 2006). A technical process should precede the participatory process. Projects are advised to develop a draft indicator set based on technical review and expert knowledge before beginning a participatory process to review possible indicators (Meadows 1998). Dynamic indicators are needed to monitor linkages and leverage points in a system. This type of indicator is especially likely to signal change or respond to action (Meadows 1998). Use standard indicator selection criteria. Common selection criteria are that the indicators be specific, measurable, achievable, realistic, timely, science-based and objective (Russillo and Pintér 2011). In addition, many projects rely on public data that are widely available to increase the transparency of indicators; and they seek indicators that are sensitive to changes in the social and natural environment (yet not so sensitive as to show excessive variability) and policy-relevant (Birkmann 2006; Gallopín 1997; Pannell and Glenn 2000). The indicators must be able to affect management decisions. Indicators are generally intended to show the status of a system or affect management decisions (Russillo and Pintér 2011); these purposes are not incompatible. Fit the indicators to their users. Different users need different indicators for different purposes; there is really no “one size fits all” indicator set. For example, in monitoring regional food system sustainability, indicators measured by farm groups will vary from those used by policy makers (Pannell and Glenn 2000; Meadows 1998). The benefit of monitoring must exceed the cost. The choice among various indicators may be determined practically by which indicator is most cost-effective. At any rate, the benefits of collecting the data must exceed the costs for monitoring to continue (Pannell and Glenn 2000). Meadows (1998) discusses the following common pitfalls in indicator selection:

• Over-aggregation in which “positives” and “negatives” are combined, such as in the GDP measure

• Measuring what is measurable, rather than what is important, such as looking at the amount of food produced rather than its accessibility or the health of the population

• Dependence on a false model, e.g., failing to recognize that the number of people participating in food assistance programs may represent increases in need, changes in eligibility criteria, or increases in the numbers of people who choose to participate

3 http://www.gdrc.org/sustdev/bellagio-principles.html.

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• Deliberate falsification, in which controversial data may be falsified or skewed • Diverting attention from direct experience, in which data and indicators blind people to direct

perceptions • Overconfidence, in which users tend to believe that indicators tell the complete story, when in

fact they are incomplete or faulty • Incompleteness, in which users forget that indicators are only models and not representative of

the actual complexity of the system In his review of different projects, Birkmann (2006) summarizes nine main steps in indicator development:

1. define goals 2. define scope, temporal and spatial boundaries 3. choose a conceptual framework (i.e. sector, issue-based or causal) 4. define selection criteria 5. identify potential indicators 6. choose final indicator set 7. collect data 8. analyze indicator results 9. assess indicator performance

These steps are followed broadly by every indicator selection project, with differences depending on how participatory each project is and how large the indicator set is. The time required for the data collection step (7) tends to be consistently underestimated and the time required to analyze indicators and assess their performance is often cut short. In addition, careful analysis of how the indicators will be displayed in order to be “user-friendly” to the attended audience is often given short shrift, although this step can be vitally important to whether indicators are useful at all. Russillo and Pintér (2011, pg. 15) expand these steps into an iterative “life cycle” of indicators leading to positive change in a system. In their framework, indicator selection and development is a single stage. It is followed by development of an information and knowledge system; communication, reporting and outreach; evaluation, learning and improvement; and development of a shared vision and conceptual framework. This last stage can lead iteratively to revised indicator selection and development; each stage should engage appropriate stakeholders. Our indicator project was unusual in some ways because of its objectives: the purpose was to allow comparison of information across states and time, so the project did not include development of sustainability goals with stakeholders. We wanted users with different goals to be able to communicate indicators. Since our project was designed to include only state level data that were available for the entire United States, questions about boundaries did not arise. And since the project was building on and extending previous work by the Wallace Center in the United States (Anderson 2009) and the Department for Environment, Food and Rural Affairs (DEFRA) in the United Kingdom, the conceptual framework of the food system and the importance of choosing indicators relevant to food system attributes of healthy, green, fair and affordable were accepted as givens. Previous National-Level Food System Indicator Projects

The United States has been keeping records of the numbers of people engaged in agriculture since 1820, when U.S. Census marshals first asked for the number of persons within each household (including

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slaves) engaged in agriculture, commerce and manufacturing as occupations and their respective characteristics. Data were collected by city or parish, district, state or territory. This census excluded “household manufacture” (Secretary of State, 1821), a category that encompassed many of the activities now allocated to different businesses that are part of the food system. In 1840, census marshals began collecting agricultural data on a separate schedule than the population census, although following the 10-year frequency of the U.S. Census until 1950, when a five-year interval started. Beginning in 1840, the aggregate value and products of agriculture were compiled by county, sometimes tracking the amount of crop produced or gathered and sometimes tracking monetary value (such as amount invested in gardens and nurseries and the estimated value of all kinds of poultry). The relevant government offices (Secretary of State, Department of Agriculture, National Agricultural Statistics Service) have continued to expand the detail of information gathered through the agricultural census over time, reflecting changing interests and concerns of the nation. For example, as a follow-up to the 2007 Agricultural Census, in 2008 the U.S. Department of Agriculture collected the first wide-scale federal survey of organic agriculture (although the Organic Farming Research Foundation had conducted four national surveys of certified organic growers beginning in 1993). The development of indicators to track environmental impacts of agricultural production was the first expansion beyond data showing the numbers of farmers, what they produced and the economic value of production. Growing public concerns over the environmental effects of agricultural practices during the 1970s and 1980s, and realization that environmental resources are limited, eventually led to creation of several sets of local, national and international governmental, intergovernmental and non-governmental agro-environmental indicators (e.g., Anderson 1994; OECD 2001; National Agri-environmental Health Analysis and Reporting Program 2011). Interest in including social aspects of agriculture in the definition of “sustainable agriculture” broadened the scope of indicators further, beyond environmental issues. This interest was propelled at the international level by the 1992 United Nations Conference on Environment and Development and its creation of Agenda 21, which advised every country to assess its progress toward sustainable development and to develop indicators, including in the domain of sustainable agriculture and rural development. Only a handful of countries have conducted comprehensive national-level food system assessments to date, using indicators to monitor attributes. DEFRA created a sustainable food and farming strategy for the United Kingdom that articulated how industry, government and consumers can work together for more sustainable farming and food industries. DEFRA’s first sustainable food and farming strategy identified nine strategic outcomes, 11 “headline” indicators to show whether each strategic outcome was met, and 45 other indicators and process measures that showed movement toward the desired outcomes (DEFRA 2002). This framework was refined by several subsequent research projects. For example, Foster et al. (2006) focused on how to reduce the environmental impact of food consumed in the United Kingdom. Their study summarized the impacts of each product category on 1) water and eutrophication, 2) energy use, 3) non-CO2 global warming, 4) processing, 5) refrigeration and packaging, and 6) other categories. Street et al. (2005) suggested indicators relevant to engagement in ethical trading by food industries in the United Kingdom. DEFRA worked intensively with food industry (manufacturers, wholesalers, retailers and food service providers) to create a Food Industry Sustainability Strategy (DEFRA 2006) to determine how the UK food industry can help advance sustainable development through adoption of best practices. They created Key Performance Indicators to measure progress in the following categories: 1) environmental (energy use and climate change,

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waste, water, food transportation) - 19 indicators; 2) social (nutrition and health, food safety, equal opportunities, health and safety, ethical trading) – 15 indicators; and 3) economic (science-based innovation, workforce skills, tackling retail crime) – 8 indicators. DEFRA has continued to refine its food system indicators, most recently through a 2009 consultative process, which resulted in the revision and publication of Indicators for a Sustainable Food System in 2011. This uses six core themes or goals:

• Enabling and encouraging people to eat a healthy, sustainable diet • Ensuring a resilient, profitable and competitive food system • Increasing food production sustainably • Reducing the food system’s greenhouse gas emissions • Reducing, reusing and reprocessing waste • Increasing the impact of skills, knowledge, research and technology

Multiple indicators addressing each theme are presented in a very readable and accessible format, which shows trends over time (DEFRA 2011). Sweden’s Food 21 Project analyzed food system sustainability in terms of its ability to satisfy Sweden’s contemporary and future economic and environmental goals, its efficiency in the use of energy and inputs, and its ability to withstand disturbance. Although economic and social aspects of the food system were included, ecological sustainability was the main focus. This project did not actually develop a set of indicators, but it articulated sustainability objectives from which indicators could be derived (Andersson et al. 2005). In Finland, a group analyzed material flow through the Finnish food system and “eco-efficiency” of its agriculture. The environmental impact of the agricultural sector of the food system was measured by indicators of agricultural land use, greenhouse gas emissions, acid emissions, fuel, electricity consumption and total material requirement. (The authors noted that nutrient outputs and biodiversity impacts were also important, but omitted). Economic indicators selected were agricultural output, agriculture’s share from GDP, employment and imports (Risku-Norja and Mäenpääb 2007). In the United States, the Center for Sustainable Systems developed a set of national-level indicators to measure the sustainability of the U.S. food system, building on a 1999 workshop (Aistars 1999). The project used life-cycle assessment to distinguish different food system activities and selected indicators from each. The indicators were organized by economic, social and environmental aspects of the following stages of the food system: 1) origin of resource - 6 indicators; 2) agricultural growing and production - 26 indicators; 3) food processing, packaging and distribution - 9 indicators; 4) food preparation and consumption - 12 indicators; and 5) end of life - 4 indicators (Heller and Keoleian 2000). More recently, the Economic Research Service of the U.S. Department of Agriculture developed a Food Environment Atlas with 168 indicators in the categories of food choices (access to, acquisition and consumption of healthy, affordable food); health and well-being (food insecurity, diabetes and obesity rates, physical activity levels); and community characteristics that might influence the food environment (e.g., demographic composition, income and poverty, metro-nonmetro status, recreation and fitness centers). Data are provided at the county, state or regional level and only the most recent data available are used. The Atlas has an interactive format that allows users to layer data and compare across geographic regions; but it does not allow users to see temporal trends because historic data are not included (Economic Research Service n.d.).

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Trends in Food System Sustainability Monitoring

The locus of food system monitoring for sustainability has moved from national-level projects by governmental or intergovernmental organizations and academicians to the private sector, with the changing domestic and international structure of the food system. This shift has led to the increasing importance of food quality standards to guarantee traceability and various attributes of food products, companies or supply networks. Companies such as Unilever have developed indicators to monitor performance throughout their food supply networks (Pretty et al. 2008). In Europe, several major food and beverage companies have joined together to form the Sustainable Agriculture Initiative Platform to share guidance on best management practices. In the United States, the Sustainable Food Lab in Vermont has led a similar consortium. Pulse Canada, the national bean production organization, commissioned a former employee of Tesco to draft “triple bottom line” indicators for the food system (Anstey 2010). Standard-setting initiatives are very relevant to indicators because they often use criterion that are equivalent to sustainability indicators, or they generate data that become indicators. That is, they can fill both the normative and descriptive functions of indicators. With widespread development and use of indicators by the private sector, the distinctions among “indicators,” “codes of conduct,” “standards” and “voluntary guidelines” have become blurry. Even if providing data on performance is initially voluntary to generate indicators of farm or company performance, some producers and members of supply networks fear that reporting might become compulsory and that meeting the performance levels of the highest-functioning businesses in the supply network may become mandatory to continue selling to a certain company, even if this leads to higher costs for the producer or smaller business. At the same time that supply chain indicators and standards are proliferating, grassroots community-based indicators of food systems are also being developed and used with greater frequency. Many communities and regions in the United States, including San Francisco, San Diego, New York City and the Delaware Valley watershed, have created food system assessments that rely on indicators (Freedgood et al. 2011). In Canada, the Food Counts project is a sustainable food system “report card” that lets communities compare how they stand with respect to over 50 indicators of sustainable food systems. “Whole measures” for community food systems (Abi-Nadr et al. 2009) have been developed in the United States by the Community Food Security Coalition; Center for Popular Research, Education and Policy; and Center for Whole Communities to facilitate planning and evaluation. Another group with a strong interest in food system indicators comprises philanthropists and agricultural development organizations seeking measurable impacts of their investments, to help them target their work more strategically. During a time of recession, informed investment in “what matters” seems increasingly important. This group does not want complex sets of multiple indicators; they want simple, “dashboard” indicators that monitor major trends and show progress or movement away from goals. As this short literature review has demonstrated, there are many separate and uncoordinated efforts to develop indicators for different purposes. Although appropriate indicators are very much dependent on the specific purposes of a project, often there is little significant learning from previous work. The Institute for International Sustainable Development developed an online compendium in 2002 that found almost 850 sustainability indicator initiatives; Russillo and Pintér (2011) estimate that the number of projects has doubled since then. There are new attempts to harmonize indicators and standards, such

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as the Sustainability Assessment of Food and Agricultural System Guidelines (FAO 2012), ISEAL Alliance’s coordination of multiple standards and their documentation of standardized ways to measure impacts; and websites that allow comparison of different standards such as the ITC Standards Map.4 Despite efforts to harmonize indicators or develop a common conceptual framework, there is still little work on food systems in contrast to agricultural production; and the existing work often lacks a clear theoretical framework that might guide hypothesis testing. Meadows’s recommendation in 1998 to develop dynamic indicators of the interactions across activities has not been implemented well for food systems yet. Without this emphasis, food system indicators tend to be large amalgamations of different kinds of data rather than indicators that show the interactions of different activities or point to possible synergies or ways that activities can be streamlined to diminish negative impacts.

3. Methods: Indicator Identification, Selection and Compilation As noted in the introduction to this report, we define a food system as an interconnected set of biological, technological, economic, and social activities and processes that nourish human populations and provide livelihood and satisfaction to the people who participate in it. As such, it encompasses activities that extend from the provision of inputs for primary food production through farming, food processing and manufacturing, food distribution and retailing, food consumption, and post-consumption food waste. The project team considered a number of different conceptual frameworks for organizing food system indicators. Ultimately, we chose to build on a framework developed by Anderson (2009) that recognizes a range of food system activities ranging from input supply, primary production, and food processing to wholesale distribution, retailing, consumption, and post-consumption waste management. Our framework also includes four aspects of food systems that describe impacts, outcomes and performance related to economic activity, the environment, individual health and society. We embraced and built on definitions established by the Anderson (2009)5 in describing these four measures while limiting the number of indicators for each state to those that could be displayed on one-page. The one-page fact sheet format represents a hybrid of the food system assessments (Freedgood et al. 2011), report cards (Food Counts) and dashboard indices discussed in the background section of this report. These food system measures are represented in the layout of the State Fact Sheet shown in Figure 3.1, with structural indicators on sales volume and employment in each food system activity arrayed across the top and indicators for each of the four performance dimensions presented in columns in the lower portion of the Fact Sheet. Linking performance indicators to specific food system activities allows for targeted discussion among community stakeholders and policymakers.

4 http://www.standardsmap.org/ 5 Anderson (2009) describes green (or environment) measures as affecting water quantity and quality, farmland quality and preservation, biodiversity, fossil fuel supply and climate change. Health is used to describe foods that are whole or minimally processed, safe, and that affect quality of life, and public treatment cost. Fairness (social) performance is described by working conditions, compensation and access. Economic performance is characterized by affordability. We build on this concept by including notions of economic activity related to production and distribution such as employment, income, and expenditures.

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Figure 3.1. Minnesota State Fact Sheet, 2012

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Each segment of the Fact Sheet has its own color code that corresponds with the indicator maps designed to show variation in indicator levels across all 50 states. Structural indicators are designated by grey or tan, economic indicators are designated by blue, environmental indicators by green, health indicators by orange and social indicators by purple. Some indicators may be characterized by multiple performance measures. For example, quantitative and qualitative studies suggest that the “percent of households with food insecurity” is a function of social and economic factors but also ultimately affects the health of the family. When encountering this situation, we choose to assign the multi-dimensional indicator to a single category. Indicator Identification and Selection

Indicator identification and compilation occurred January 2010 – January 2011. Indicators were updated in 2016 using 2012 Census data. Indicators were identified through a literature review and through an extensive interdisciplinary iterative process as is common in indicator research (Russillo and Pintér 2011). This identification process involved project team members, colleagues and professionals with expertise in economic, environmental, health, and social performance measure areas. Our initial objective when identifying indicators was to describe structural characteristics and economic, environmental, health and social performance for each food system activity (input supply, farming, processing, distribution and wholesaling, retailing and consumption, waste and recovery). However, this proved unworkable due to data limitations for activity-specific indicators in the four performance areas. Instead, we resolved to compile structural indicator data for each food system activity and performance measures for the food system as a whole. The literature review yielded more than 100 national-, state-, and county-level food system indicator measures. The majority of these measures were characterized as economic and environmental. Our study team built upon this initial suite of indicators by attempting to fill indicator gaps, particularly in the health and social performance areas, based on our respective disciplines (economics, environmental sciences, health sciences and social sciences), working knowledge of these topics and on current national food system priorities. Team members were asked to “dream” a bit during this process – to generate a comprehensive, though not exhaustive, list of potential indicators that would produce measureable indicators. Ultimately, this yielded another 106 unique6 indicators. Economic and social performance measures account for almost 60 percent of the total indicator pool of 215 indicators identified. The complete list of potential indicators is presented in “Appendix A: Indicators.” Three data selection criteria were applied to the list of 200+ indicators: continuity and consistency; accessibility and geographic scope and scale. Generally, indicators failing to satisfy any one of these data criteria were rejected. Continuity and Consistency: Time series analysis and study replicability necessitate the use of data that are continuously and consistently collected over time. Our data time frame is 1997-2012. Initially we anticipated collecting annual data, however, due to limitations in availability, we determined to collect data for the years 1997, 2002, 2007, and 2012 that correspond with the Economic Census schedule. Generally, if data were unavailable for an indicator during one of the three Census years identified, the indicator was rejected. However, we did include two indicators for which data are unavailable prior to 6 “Unique” refers to the single count of an indicator although it may be applied multiple times (to various performance measures and/or to various food system activities). For example, the structural indicator – “number of establishments” – is counted as one indicator even though it is applied to seven food system activities in the state fact sheet.

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2002 (but which are slated for future collection at five year intervals). These indicators are the “Percent of Farmland Treated with Manure” and the “Percent Municipal Solid Waste Recycled.” The abbreviation “NA” appears on 1997 state fact sheets for these two indicators and again on 2012 state fact sheets for “Percent Municipal Solid Waste Recycled.”

Accessibility: Universal access to information is fundamental to our selection criteria. Indicators were considered for the study only if representative data were transparent and available at no cost to users. This is one reason for strong reliance on public data collected by government sources. At the time of this writing (August 2016), all indicators selected for our study are obtainable without fee from Internet databases.

Geographic Scope and Scale: The geographic scope for our study is the United States. Data for many selected indicators were available at different scales (metro, county, state and nation). We rejected any indicator for which state level data were unavailable. This geographic criterion did limit our indicator selection - primarily in the areas of health and environmental performance. Per capita fruit and vegetable consumption data, for example, were available only for major metropolitan areas, while several environmental indicator data were available only at the watershed level. (We choose not to extrapolate data from watersheds as many states straddle or encompass multiple bioregions.) Approximately half of the indicators failed to satisfy all three of our selection criteria. Our list of potential indicators was reduced further through the elimination of those indicators representing nationally established markets (e.g., oil and gas), immature markets (e.g., “organic farm acreage” and “direct sales to consumers”) as well as those indicators which generally are not sensitive to change over time (e.g., “average land slope”) or which we considered weakly related to the food system (e.g., “voter participation rates”). Some of these decisions were difficult to make but ultimately were necessary to end with the desired one-page fact sheet. We concluded the data selection process with the 63 unique indicators listed on the state fact sheets. All indicators are presented in their most common format – reflecting both positive and negative movement over time. Consequently, a positive indicator that grows over time may not be considered “better.” For example, we report the indicator “percent of population obese” as measured by the Centers for Disease Control. As this number grows (moving in a positive direction) it reflects an undesirable outcome. Data Sources

Data identified and compiled for the project come from secondary sources. (We did not generate primary data.) Many public sources were considered when identifying indicators such as state and federal governmental and non-governmental institutions. Ultimately, we drew upon databases maintained primarily by federal agencies, such as those containing Census results, as these best satisfied our selection criteria. We cast our net wide when exploring government data sources - examining everything from decennial nationwide surveys to one-time state level polls. Key data sources include the U.S. Census Bureau, the U.S. Department of Agriculture (USDA), the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Centers for Disease Control. The two most frequently used sources of data employed in our study are the U.S. Census Bureau’s Economic Census and the USDA’s Census of Agriculture – both of which code data using the North American Industry Classification System (NAICS) allowing for consistent data compilation across food system activities. One non-governmental source also is utilized - BioCycle Magazine - which produces the “State of Garbage Report” in collaboration with Colombia University. For a complete list of data sources, see “Appendix B: Indicator Definitions, Sources and Compilation Methods.”

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Data Compilation, Imputation and Normalization

Data entry was conducted by a team of undergraduate students and staff from the College of Food, Agricultural and Natural Science at the University of Minnesota. Data were compiled by downloading directly from online government databases whenever possible. However, in some cases we were required to hand-enter data state-by-state. Data conversions and imputations were necessary when: 1) data within a particular NAICS code included economic activity that could not be attributed only to the food industry thus over-valuing the indicator data and/or 2) data were unavailable for a particular state or industry due either to non-reporting or aggregation by the original data provider. For example, when measuring sales from “warehouse clubs and supercenters” it was necessary to remove sales attributable to non-food/drink categories (e.g., tobacco, sundries, household items). Time constraints prevented us from collecting line item data from food- and drink-related merchandizing categories as is provided in Merchandizing Line Sales reports (Evans et al. 2001). Instead, we developed and applied weighted estimates of food/drink and non-food/drink sales using the Food Institute’s Food Industry Review. Likewise, several data sets from the Economic Census are incomplete due to state non-reporting or to aggregation by the original data provider to protect source confidentiality. When this occurred, we imputed weighted values (e.g., employment, wage, and sales data). All data was reviewed and cleaned before being imported to a database using Microsoft Access software for creation of state fact sheets and a mapping program using ArcGIS software for creation of the indicator maps. However, we recognize that compilation and computation errors may be present due to human error. Many of the structural indicators and several performance indicators represent census counts (e.g., numbers of people or volumes of sales). In order to make meaningful comparisons possible, it is often necessary to normalize the data. For example, we normalize “very large farm acreage” by total “farm acreage” to arrive at the proportional value of “very large farm acreage/total farm acreage” within states that are land rich and land poor alike. In some cases, there is more than one variable that could be used for normalization. In this instance, we choose the normalizing variable that offered the most contextual information for the descriptive or performance measure at hand. Key normalizing variables used are: population, state gross domestic product, and acreage, agricultural product sales and net farm income. For more information about data conversions and normalization, see “Appendix B: Indicator Definitions, Sources and Compilation Methods.”

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4. A Guide to Using the Food System Indicator Resources The primary objective in this project was to develop indicator resources that can be easily accessed and used by scholars, educators and other engaged citizens as a basis for learning about the food system and as a tool for stimulating discussion about food system changes, values, policies and outcomes. The indicator data compiled for this project help identify important differences and trends in food system structure, size and performance across states and over time. The accompanying state fact sheets and indicator maps summarize the data and facilitate food system analyses. Providing a complete discussion of all the findings embodied in these resources would be a monumental task that goes far beyond the scope of this project. However, we can make an important contribution for future users by showing how the data, fact sheets and maps can be used to support research, education, and civic engagement activities. This section of our project report is a “user’s guide” that illustrates a number of ways these resources can be utilized. We begin this guide by showing how a state fact sheet can be used as the starting point for an exploration of the food system in a particular state. Here the focus is on identifying general patterns related to size, structure, and performance of the food system and on comparing a state to its neighbors. Next we present a series of examples that illustrate how to use indicator maps to examine differences in the level of a single indicator across states and over time. Here the focus is on developing an in-depth understanding of one aspect of the food system. For this purpose, we selected one indicator from each of the major indicator categories in our conceptual framework, basing our selection not only on interest and importance but also on the illustrative value of findings. Finally, we illustrate how relationships among indicators can be explored systematically using regression analysis. Here the focus is on identifying meaningful patterns in indicator levels and important associations among indicators. This “user’s guide” is accompanied by a sample PowerPoint presentation and poster that show how all these approaches to using the indicator data can be integrated into presentations. The complete indicator data set is also available to be downloaded. All these resources are available on the project web site. Exploring the Food System from a State Perspective

One of the most common uses we envisage for the food system indicator resources developed under this project is the examination of the food system from the perspective of an individual state. The purpose could be to simply develop a better understanding of the size and structure of the food system. Alternatively, the purpose could be to document key state level trends in the food system as a starting point for policy discussions or for more in-depth research on a particular aspect of food system size, structure, or performance. We will use Minnesota to illustrate how the indicator resources can be used for these purposes. Indicator data and resources referenced are available for Minnesota and all states on the project web site. The Minnesota state fact sheet for 2012 is reproduced in Figure 4.1. As noted earlier, the color-coded sections correspond to the five major elements in the organizing framework for the indicators: size and structure and food system status and performance in economic, environmental, health, and social dimensions.

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Figure 4.1. Minnesota State Fact Sheet, 2012

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The size and structure data at the top of the fact sheet is almost always a good place to start. Looking first at the far-right column that gives total employment and payroll figures for all activities within the food system, it is noteworthy that 22.4 percent of all jobs in the Minnesota economy and 12.5 percent of total payroll are linked to the food system as it is defined for this project. While Minnesota’s population of nearly 5.4 million people represented only about 1.7 percent of the U.S. population in 2012 (estimated by the U.S. Census Bureau to be 313,914,040), food system employment in Minnesota accounted for 2.3 percent of all jobs and 3.1 percent of all payroll in the U.S. food system. Looking more closely across the food system sectors, most of the food system jobs are in farming, food processing and retailing. Compared to the $42,348 state average payroll per employee across sectors of the Minnesota economy, average payroll per employee is relatively low in all of the food system sectors except distribution & wholesaling. Average payroll per employee is especially low in food retailing, due to the large number of part-time jobs in this sector. Minnesota’s average payroll per employee levels are above national sectoral averages for farming, processing and distribution & wholesaling, slightly below average for input supply and retailing, and well below average for waste & recovery. Looking down to the Economic Indicators section, the fact that retail food sales in Minnesota account for 8.0 percent of state GDP is noteworthy because this figure is considerably less than the food system share of jobs and payroll. This suggests that Minnesota’s food system is creating value that is exported to other states. Changes in the Food System over Time: Many of the other indicators in the state fact sheet are difficult to interpret without placing them in the context of how they have changed over the years or how they compare with levels for other states. We look first at changes in indicator levels over time for Minnesota, focusing on the ten-year period from 2002 through 2012. Figure 4.2 compares basic state characteristics from the top of the state fact sheets for 2002, 2007, and 2012.7 Population rose steadily over this period. Average payroll per employee rose by 2.7 percent between 2002 and 2007 but then leveled off as a result of the recession in 2008. Total employment and total payroll actually fell between 2007 and 2012. Looking at the figures on the right side, land in farms and the percent of land in farms declined slowly but steadily over the decade. Farm numbers were steady between 2002 and 2007 but then fell by 8.0 percent between 2007 and 2012. Figure 4.3 presents size and structure indicators for Minnesota in 2002 and 2012. The food system share of employment within the state, both for the entire food system and for individual sectors, has remained relatively stable, though there was a slight decrease for farming and a slight increase for retailing. The food system share of annual payroll in the state increased from 8.7 percent in 2002 to 12.5 percent in 2012. This was due largely to a dramatic increase in farm profits (which are included in farm payroll) between 2002 and 2012. Therefore, this may not indicate a significant trend. Looking more closely at individual sectors, the number of employees rose in input supply, retailing, and waste & recovery but fell for farming and processing. Figure 4.4 presents Minnesota’s economic indicator levels for 2002, 2007, and 2012. Indicators in the top portion of this section of the state fact sheet characterize the farming sector of Minnesota’s food system. The value of ag sales increased steadily over this period, largely due to higher commodity prices.

7 Segments of the state fact sheets can be extracted and inserted into a document or presentation by using the “crop” function on the full state fact sheet pdf file.

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Figure 4.2. Minnesota State Characteristics: 2002, 2007, and 2012

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Figure 4.3. Size and Structure Indicators for Minnesota: 2002 and 2012

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Figure 4.4. Economic Indicators for Minnesota: 2002, 2007, 2012

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The percent of sales attributable to livestock and livestock products was steady between 2002 and 2007 but then declined sharply in 2012. This may reflect a meaningful trend but could also be due to weather events or significant changes in relative prices. Net farm income as a percent of agricultural sales rose sharply over this period, and government payments as a percentage of ag sales fell significantly, as both 2007 and 2012 were excellent years for farm profitability. Agricultural R&D expenditures (another form of public support for agriculture) increased substantially between 2002 and 2007 but then fell sharply. Finally, farm income concentration, the portion of agricultural sales accounted for by the top three commodities, increased steadily over this period. Indicator levels in the lower portion of Figure 4.4 characterize the retail food sector of Minnesota’s food system. Retail food expenditures as a percent of state GDP declined and then rose slightly from 2002 to 2012, perhaps as a result of rising and then falling total payroll levels in the state over this period. The share of retail expenditures made in grocery stores declined significantly over this period, from 43.7 percent in 2002 to 36.8 percent in 2012. This is due, in part, to a sharp increase in the percentage of food sales made by supercenters and wholesale clubs. It also reflects a shift to more food being purchased and consumed away from home in restaurants and special food service facilities. Note, however, that the share of food expenditures made for food away from home declined slightly from 2007 to 2012. This is likely due to residual effects of the recession of 2008. Figure 4.5 presents Minnesota’s environmental indicator levels for 2002, 2007, and 2012. The percent of farmland enrolled in conservation programs rose and then fell sharply over this period, while both water and wind-related soil erosion declined. The real value of crop chemicals purchased per acre declined slightly between 2002 and 2007 – from $17.99 to $16.71 per acre – but then rose to $21.84 per acre in 2012. Over the same period, the real value of fertilizers, lime, and soil conditioners more than doubled – from $26.36 to $54.83 per acre. The increase in fertilizer purchases may be due to rising prices for major field crops, which is an incentive for using more fertilizer in order to increase yields. The percent of farmland treated with manure increased slightly from 2002 to 2012. Data for the percent of municipal waste recycled are difficult to interpret due to changes in the way this indicator is defined and compiled by original sources. Figure 4.6 presents Minnesota’s health indicator levels for 2002, 2007, and 2012. Although the percent of the population that is overweight declined slightly over this period, this is the result of many people moving into the obese category, which rose from 22.4 percent in 2002 to 37.3 percent in 2012. This change has been paralleled by a sharp increase in the percent of adults with diabetes – from 4.9 percent in 2002 to 10.6 percent in 2012. The percent of households with food insecurity rose sharply between 2002 and 2012, due in part to the recession of 2008. Job-related illness and injury data per 10,000 full-time employees provide insights on another health related dimension of food system performance. These indicators can be quite variable from year to year, but they exhibit a general downward trend in all sectors except waste & recovery. Finally, pounds of meat and poultry recalled per meat and poultry processing establishment is also highly variable, since recall events are relatively rare. There is no consistent trend in this indicator for Minnesota. Figure 4.7 presents Minnesota’s social indicator levels for 2002, 2007, and 2012. The first four of these indicators refer to the farm sector. The percent of farmers whose principal occupation is farming declined significantly between 2002 and 2007 but then rose slightly in 2012 to a level that remains well below that for 2002. One explanation for this is that the number of small part-time farms has increased.

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Figure 4.5. Environmental Indicators for Minnesota: 2002, 2007, 2012

2002 2007 2012

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Figure 4.6. Health Indicators for Minnesota: 2002, 2007, 2012

2002 2007 2012

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Figure 4.7. Social Indicators for Minnesota: 2002, 2007, 2012

2002 2007 2012

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The average age of farmers increased slightly from 2002 to 2012. This is a continuation of a long term trend. Both the percent of farms classified as “very large” and the percent of farm acreage accounted for by these very large farms increased substantially between 2002 and 2012. This points to increased concentration in the farm sector, but it is also partly the result of higher commodity prices that help farms reach a higher sales class. The percent of the population receiving SNAP (formerly known as food stamp) benefits more than doubled from 2002 to 2012, largely due to impacts of the recession in 2008. Finally, the last six indicators show changes in the number of retail food establishments per 10,000 people. Noteworthy trends are the very large percentage increase in the number of supercenter and wholesale club stores, a steady decline in the number of convenience stores, and a sharp decline in the number of limited service restaurants per 10,000 people between 2007 and 2012. Comparing the Food System across States within a Region: Up to this point, the discussion of state level indicators from the perspective of a single state has focused on changes over time. This does little, however, to provide perspective on how a single state compares with other surrounding states or with national patterns in food system size, structure, and performance. One way to compare states is to simply present sections of their respective state fact sheets side-by-side, as has already been done for a single state over time. Additionally, we offer several other ways to make comparisons across states. The indicator maps that will be discussed in the next subsection are very effective for cross-state comparisons focusing on single indicators. In the remainder of this subsection, however, we focus on graphs that present data on multiple indicators for multiple states. These graphs were constructed using data extracted from the Excel worksheet that is available on the project web site. They focus on comparing indicators for Minnesota to those for surrounding states. The number of combinations of states and indicators that could be used in constructing graphs like these is almost limitless, so those presented here are simply illustrative of what is possible. Figure 4.8 is a column chart that compares the distribution of jobs across the six food system sectors in 2012 for Minnesota and its surrounding states, as well as for the entire United States. This graph reveals several noteworthy patterns. First, the largest sectors in the food system in terms of employment are consistently farming and food retailing. As was noted earlier, this is partly due to the fact that there are many part-time jobs in both of these sectors. In addition, all farm operators are included in the employment level for the farming sector, but only a relatively small proportion of farmers consider farming to be their principal occupation. Second, all of the states in the Upper Midwest have shares of state employment dedicated to farming and food processing that are larger than national shares for these respective sectors. Figure 4.9 is a stacked column chart that compares the share of retail food sales by marketing channel in 2012 for Minnesota and its surrounding states, as well as for the entire United States. Here the shares for each geographic location add to 100 percent, so the columns are the same height for each location. The “Other” category represents sales made in liquor stores and bars. As noted in Appendix B, we did not exclude sales of alcoholic beverages from total food sales. However, we do not report sales levels for outlets that only sell alcoholic beverages in this portion of the state fact sheet.

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Figure 4.8. Distribution of Employment across the Food System for Minnesota and Surrounding States in 2012

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Figure 4.9. Share of Retail Food Sales by Marketing Channel for Minnesota and Surrounding States in 2012

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Relative to the US average, the share of food sales through grocery stores (which include supermarkets) is slightly lower in Minnesota and North and South Dakota but higher in Wisconsin and Iowa. The share of food sales through convenience stores is higher than the US average for all five Upper Midwestern states. Supercenter and wholesale clubs have shares of food sales in Wisconsin and Iowa that are below the national average. The share of retail sales for food away from home – limited service restaurants, full service restaurants, and food service establishments – is lower than the national average in all of the Upper Midwestern states. Finally, the share of retail sales made through “Other” stores ranges from three to 11 percent. This percentage is sensitive to laws regarding the sale of alcoholic beverages in grocery stores. It is highest in Minnesota and North Dakota. Both these states prohibit wine and distilled beverage sales in grocery stores. Minnesota allows sale of 3.2 beer, but North Dakota prohibits all alcohol sales. Iowa and South Dakota have the lowest percent of sales through the “Other” channel. Both of these states allow sale of beer, wine and distilled beverages in grocery stores. In Wisconsin regulation of alcohol sales in grocery stores is by local ordinance. Figure 4.10 is an area chart that compares the number of retail food establishments per 10,000 people in each retail marketing channel in 2012 for Minnesota and its surrounding states, as well as for the entire United States. This graph also shows the total number of establishments across channel types. The number of grocery stores per 10,000 people is relatively small for all locations, but it is slightly higher in South and North Dakota, the two least densely populated states. Relative to the national average, these two states, along with Iowa, also have large numbers of convenience stores per 10,000 people. The number of supercenters and wholesale club stores is quite small across all states. Looking back at Figure 4.9 for the share of retail sales for this marketing channel, this suggests that these are very high sales volume stores. All five Upper Midwestern states have fewer limited service restaurants per 10,000 people than the national average, but the number of full service restaurants per 10,000 people is above the national average in all the Upper Midwestern states except Minnesota. Finally, it is noteworthy that the total number of retail establishments per 10,000 people, which is indicated by the overall height of the shaded area, is below the national average in Minnesota and well above the national average in South Dakota. Exploring Spatial and Temporal Variation for a Single Food System Indicator

Another common usage that we envisage for the food system indicator resources developed under this project is the examination of differences in the level of a single indicator over space and time. Here the objective could be to identify important regional differences for a key aspect of food system performance or to determine whether a trend observed for one state is also happening in most other states. Such analysis can be the starting point for developing hypotheses on situational factors or policy differences that are associated with performance differences. Indicator maps are the most convenient tool for this type of analysis. Maps are available on the project web site for each indicator for 1997, 2002, 2007, and 2012, as well as for the change in each indicator level from 1997 to 2012. In this subsection we present maps for one indicator selected from each major indicator categories in our conceptual framework. In some cases, we supplement the maps with graphs based on the indicator data. The indicators discussed here were chosen not only because of their importance but also for their illustrative value.

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Figure 4.10. Retail Food Establishments per 10,000 People by Marketing Channel for Minnesota and Surrounding States in 2012

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Figure 4.11. Food System Employment as a Percent of State Employment in 2012

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Figure 4.12. Supercenter/Wholesale Club Sales as a Percent of Retail Food Sales in 2012

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Size and Structure: One of the most important findings derived from the size and structure indicator data is that the food system plays such an important role in the broader economy. Figure 4.11, which shows the percentage of total state employment attributable to the overall food system, illustrates this effectively. While there is considerable variation in the share of total state employment attributable to the entire food system, that share is greater than 15 percent in all but a few states along the eastern seaboard. The food system share of total state employment is more than 30 percent in Iowa and in a cluster of states in the northern Great Plains. Economic Indicators: The emergence of supercenters and wholesale club stores has had an important impact on the retail food sector. Figure 4.12 shows variation in the percent of total retail sales accounted for by these stores in 2012. Total sales volume in this segment of the sector has grown rapidly, and Walmart is now the largest food retailer nationally and worldwide. The importance of supercenter and wholesale club stores differs across states, however. These stores account for less than 7.5 percent of sales in several New England states, as well as in California and Wisconsin. On the other hand, these stores account for more than 14 percent of sales in 31 states. Environmental Indicators: Figure 4.13 shows variation in the percent of farmland enrolled in conservation programs in 2012. Nationally, the percent of farmland enrolled in these programs was about three percent in 1997, 2002, and 2012 but rose to over four percent in 2007. In 2012 enrollment was especially high across the northern tier of states extending from Minnesota to Washington; in states extending down the Mississippi River from Minnesota; in Kansas and Colorado, and in the tier of states in the southeast that have wetlands and estuaries along the Gulf of Mexico and the Atlantic, with Florida as a notable exception. The New England states that have less than one-half of one percent of farmland enrolled in conservation programs are states that have a very low percentage of total land area in farms. West Virginia stands out as an outlier relative to its surrounding states. Health Indicators: In some cases, valuable insights can be gained by looking at a sequence of maps for a single indicator. The color scale used for the maps is the same across years, so they can be compared directly. Figure 4.14 shows how the prevalence of adult diabetes changed from 1997 to 2012. This progression parallels the dramatic rise in rates of overweight and obesity that are often illustrated with “animated” maps. In 1997 there were only a few states in which more than 5.5 percent of adults had diabetes. That changed rapidly, however, and by 2012 more than ten percent of adults had diabetes in more than 20 states, and the percent of adults with diabetes was at least seven percent in all the states. Social Indicators: Figure 4.15 shows changes in the percent of the population receiving SNAP benefits between 1997 and 2012. Changes in this percentage may be due to changes in overall economic conditions or to efforts to increase participation in the SNAP program by those households who are eligible for it. Knowledge of the starting point for the percentage of the population receiving SNAP benefits can help put these changes into perspective. The scatter plot in figure 4.16 is one way to display information on the beginning and ending levels for an indicator as well as the magnitude of the change. The horizontal axis in this graph represents the percent of the population receiving SNAP benefits in 1997, while the vertical axis represents that percentage in 2012. There is one point for each state, and it is located horizontally at its 1997 level for this indicator and vertically at its 2012 level. The point for a state with no change would be along the black 45 degree line labeled “No Change.” The point for a state with a decrease in the percent of its population receiving SNAP benefits will be below the “No Change” line, while the point for a state with an increase for this indicator will be above that line. Larger deviations from the “No Change” line represent larger increases or decreases. Note that the percentage of the population receiving SNAP benefits increased in all 50 states, largely due to the recession in 2008.

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Figure 4.13. Percent of Farmland Enrolled in Conservation Programs in 2012

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Figure 4.14. Percent of Adults with Diabetes, 1997, 2002, 2007, and 2012

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Figure 4.15. Change in Percent of Population Receiving SNAP Benefits from 1997 to 2012

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Figure 4.16. Scatter Plot of Percent of Population Receiving SNAP Benefits in 1997 and 2012

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The points in this graph are color-coded to represent different regions of the country. Most of the Northeastern states, which are represented by blue diamonds, stared out with relatively low percentages of their populations receiving SNAP benefits and all had moderate increases. All the Midwestern states, which are represented by red squares, had relatively low levels for this indicator in 1997. Most had experienced large increases by 2012. The largest increase was from eight percent to 19 percent in Michigan. Most of the states in the South, which are represented by green triangles, started out at relatively high levels for the percent of their population receiving SNAP benefits. Florida and Georgia had 11 percent increases in the percent of the population receiving SNAP benefits, while Delaware, Mississippi, North Carolina, South Carolina, and Tennessee all had 10 percent increases. Finally, in the West, Oregon had a 14 percent increases in SNAP participation, and Arizona and New Mexico had 11 percent increases. Regression Analysis Based on the Indicator Data

This section of the user’s guide illustrates how the indicator data can be used in a regression analysis to explore relationships among indicators. Regression analysis is a statistical method for estimating a linear relationship between a dependent variable and set of explanatory or independent variables. For the sake of illustration, we consider the following general model of the relationship between the percent of the population that is overweight or obese and a set of explanatory variables that provide at least a partial description of the “food environment” in a state at a particular point in time. Oit = b0 + b1Yearit + b2FoodInsecureit + b4SNAPit

+ b5Groceryit + b6CStoreit + b7SuperCenterit + b8LimitedServiceit + b9FullServiceit + b10FoodServiceit + εit

where:

Oit is the percent of the population that is (a) overweight, (b) obese, or (c) overweight or obese in state i in year t.

Yearit is the year (1997, 2002, 2007, or 2012) for state i in year t. FoodInsecureit is the percent of households that are food insecure in state i in year t. SNAPit is the percent of the population receiving SNAP benefits in state i in year t. Groceryit is the number of grocery stores per 10,000 people in state i in year t. CStoreit is the number of convenience stores per 10,000 people in state i in year t. SuperCenterit is the number of supercenter and wholesale club stores per 10,000 people in state

i in year t. LimitedServiceit is the number of limited service restaurants per 10,000 people in state i in year

t. FullServiceit is the number of full service restaurants per 10,000 people in state i in year t. FoodServiceit is the number of special food service establishments per 10,000 people in state i in

year t. εit is a random error term. b0, b1, …, b10 are parameters to be estimated.

This is actually three models, since there are three possible dependent variables: the percent of the population that is overweight, the percent of the population that is obese, and the percent of the population that is overweight or obese (which is the sum of overweight and obese). We include the year

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in order to capture general trends in the data that are not explained by changes in the other variables. We expect the estimated parameter for this variable to be positive. FoodInsecure and SNAP measure the prevalence of low income households in a state. Many studies have suggested that these are positively related with overweight and obesity. If true, the estimated parameters for both of these variables should be positive. The remaining variables measure the average availability of different types of grocery and food service outlets in a state. It is often suggested that ready availability of convenience stores and limited service restaurants is associated with high levels of overweight and obesity. If so, the parameters for CStore and LimitedService should be positive. Regression results for the three models are presented in table 1. Looking first at the regression statistics for the three models in the upper portion of the table, the explanatory power of all three models is high. The R2 statistic is a measure of the percentage of total variation in the dependent variable that is explained by the model. The “Overweight” model explains more than 93 percent of the variation in the percent of the population that is overweight over time and across states. The “Obese” and “Overweight or Obese” models explain 70.3 and 81.4 percent of the variation in their dependent variables over time and across states. Turning attention to the parameter estimates in the lower portion of the table, many of the parameter estimates in each of the models have standard errors that are small relative to the estimates themselves, which implies that we can place high confidence in them. The P-value is the probability that we would be wrong if we stated that the parameter is not equal to zero – i.e., the probability that we would be wrong to state that the associated explanatory variable is correlated with the dependent variable. As expected, the parameter estimate for Year is positive and significantly different from zero for the “Overweight” model, but it is negative and significantly different from zero for the “Obese” model. The estimates imply that, holding all other factors constant, the percent of the population that is overweight is increasing by an average of 1.146 percent each year, while the percent of the population that is obese is decreasing by an average of 0.445 percent each year. The upward trend in the “Overweight” model could be the result of people who were not previously overweight gaining weight to become overweight, the result of people who were obese losing weight to become overweight or both. Note that the parameter estimate for Year in the “Overweight or Obese” model is simply the sum of the parameter estimates for the “Overweight” and “Obese” models. This estimate implies that, on average, the percent of the population in these two weight categories has been increasing at a rate of 0.701 percent per year. Contrary to expectations, the parameter for FoodInsecure is negative in all three models and is significantly different from zero in the “Obese” and “Obese or Overweight” models. As expected the parameter estimate for SNAP is positive and significantly different from zero for the “Overweight” model, but it is not significantly different from zero in the other two models. The parameter estimates for CStore and SuperCenter are positive in all three models, though the estimate for the CStore parameter is not significantly different from zero in the “Overweight” model. These results imply that

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Table 1. Regression Results for Overweight/Obesity Models

Overweight Obese Overweight or Obese Observations 200 200 200

R2 0.938 0.716 0.822 Adjusted R2 0.935 0.703 0.814 Standard Error 1.883 2.328 2.211 F 321.108 53.248 97.499 F Significance 0.0000 0.0000 0.0000

Coefficients Std Error P-value Coefficients Std Error P-value Coefficients Std Error P-value

Intercept -2266.195 84.605 0.0000 920.688 104.592 0.0000 -1345.507 99.329 0.0000 Year 1.146 0.042 0.0000 -0.445 0.052 0.0000 0.701 0.050 0.0000 FoodInsecure -0.105 0.073 0.1509 -0.190 0.090 0.0360 -0.295 0.086 0.0007 SNAP 0.190 0.056 0.0009 -0.086 0.069 0.2161 0.104 0.066 0.1157 Grocery 1.058 0.262 0.0001 -0.345 0.324 0.2887 0.713 0.308 0.0216 Cstore 0.090 0.114 0.4298 0.800 0.141 0.0000 0.891 0.134 0.0000 SuperCenter 5.862 2.567 0.0235 7.017 3.174 0.0282 12.879 3.014 0.0000 LimitedService -0.829 0.096 0.0000 1.175 0.118 0.0000 0.346 0.112 0.0023 FullService -0.454 0.105 0.0000 -0.555 0.130 0.0000 -1.009 0.124 0.0000 FoodService 0.033 0.396 0.9344 -0.575 0.490 0.2424 -0.542 0.465 0.2457

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there is a positive correlation between the prevalence of these store types and the incidence of overweight and obesity. The parameter for LimitedService is negative and significantly different from zero for the “Overweight” model but is positive and significantly different from zero in the other two models. In interpreting these results and those of other regression analyses using the indicator data, it is important to keep at least two caveats in mind. First, model specification is of critical importance, especially when many of the indicators may be highly correlated with other indicators not included in a model. Omission of relevant variables and/or choice regarding functional form may significantly affect results. Second, it is important to remember that regression results show statistical association but are not necessarily indicative of causation. Therefore, initiating a policy intended to shift an indicator up or down may not necessarily have the desired effect. Nevertheless, regression analysis is a useful tool for exploring complex relationships among food system indicators. Presenting Results

The state fact sheets, indicator maps, and other supplementary graphs are all individual summary tools that can be integrated into more comprehensive presentations designed to educate an audience on the size, structure, and performance of the food system; to stimulate discussion on issues related to the food system; or to present more detailed findings from an analysis of a selected set of indicators. The “Sample Presentations” section of the project web site includes a page with links to a sample PowerPoint presentation and a sample poster that were developed for the first version of this user’s guide. Each uses the 2007 Minnesota state fact sheet as a starting point. In addition, there is an updated PowerPoint presentation that uses the 2012 Minnesota state fact sheet as a starting point and includes many of the figures presented in this user’s guide. These are just illustrations of what one can do with the resources developed through this project. We are confident that others will have many more ideas for creative ways to use these resources. That page also includes links to spreadsheets that were used to create all the supplemental graphs and to perform the regressions that were presented in previous sections.

5. What’s Next: Considerations for Future Research The primary goal of our project was the creation of indicator tools that would allow stakeholders to understand and assess food system performance over time. The indicator tools - fact sheets and maps – provide the resources needed to understand food system structure and assess change. However, much like other indicator projects, they do not incorporate a feedback loop of any sort to monitor how the indicators inform goals or to assess the outcome of indicator-driven actions. This was well beyond the scope of our study, but we acknowledge that the real test of indicators is whether they are used to make a difference in policies and move the food system toward goals, including greater sustainability. There is a large gap in the literature on indicator evaluation or studies of how they are used and by whom. The three-year POINT project in the EU (Policy Influence of Indicators)8 that ended in spring of 2011 is an exception; it investigated how indicators have actually affected policy.

8 http://cordis.europa.eu/project/rcn/89898_en.html.

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We see several opportunities for future research that can address this need and build on our project. These are:

1. Answering questions about “how” indicators are used and by whom. As civic groups, educational institutions and policymakers begin using the indicator tools, we encourage them to chronicle observations and tool use, record dialogue and findings, to share presentations that may be created using the ideas described in the user’s guide or otherwise and, most importantly, to share how indicators are being used to develop goals, implement ideas and make decisions.

2. Continuing Monitoring and Data Compilation. As new data are released from the 2012 Census

(and beyond), indicator fact sheets can be updated by researchers to support on-going food system monitoring and indicator assessment. A spreadsheet with the indicator data used for the state fact sheets and maps is provided on the project website along with information in “Appendix B: Indicator Definitions, Sources and Compilation Methods” detailing data sources, compilation techniques and conversion methods.

3. Creating New Indicators. Nearly all indicator research concludes with a “wish list.” The Wallace

Center, for example, drafted a comprehensive list of recommended food system attributes and “promising [data] innovations” that could broaden and enrich food system monitoring by including topics such as food democracy, production transparency, regional food marketing, animal welfare, food waste, corporate social responsibility, social capital, and cultural aspects. While our initial list of more than 200 indicators does not stretch beyond the performance measures outlined in our research design, it does suggest an expanded range of structural, economic, environmental, health and social indicators within close reach. For example, many of the indicators included as “other indicators identified” in “Appendix A: Indicators” are available at the national level or for one or more years beginning sometime after our 1997 data collection “start date.” While these indicators did not meet our data selection criteria, they offer a promising glimpse at future indicator monitoring.

Active dialogue and wise use of indicators may help to shift food systems from negative to positive contributors to sustainability. We look forward to the exchange of information and ideas that will surely accompany the advancing pace of indicator research.

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Appendix A: Indicators Indicators included in this appendix represent the complete list of descriptive indicators as well as structural, economic, environmental, health and social performance indicators identified for our study through literature review and a brainstorming process. Indicators are organized by food system activity and listed alphabetically. Performance measure indicators are embedded within each activity and coded accordingly (structural (ST), economic (EC), environmental (EN), health (H) and social (SO)). Descriptive

• Average payroll/employee • Land area • Land in farms • Number of farms • Percent land in farms • Population • Total employment • Total payroll

Input Supply

• Annual payroll (ST) • Annual payroll as % of state payroll (ST) • Annual payroll as % of sector payroll (ST) • Average payroll per employee (ST) • Average payroll as % of state payroll(ST) • Average payroll as % of sector payroll (ST) • Employment as % of state employment (ST) • Employment as % of sector employment (ST) • Number of establishments (ST) • Number of illnesses and injuries per 10,000 full-time employees (H) • Number of paid employees (ST)

Farming

• Agricultural R&D expenditures as percent of agricultural sales (EC) • Annual payroll (ST) • Annual payroll as percent of state payroll (ST) • Annual payroll percent of sector (ST) • Average age of farmers (SO) • Average payroll per employee (ST) • Average payroll as percent of state payroll (ST) • Average payroll as percent of sector payroll (ST) • Crop sales as percent of agricultural sales (EC) • Farm income concentration (EC) • Fruit and tree nut sales as percent of crop sales (EC) • Government payments as percent of agricultural sales (EC) • Hired farm labor expense (included in annual payroll) (ST) • Livestock and product sales as percent of agricultural sales (EC)

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• Net farm income as percent of agricultural sales (EC) • Number of paid employees (ST) • Number of employees as percent of state employment (ST) • Number of employees as percent of sector employment (ST) • Number of illnesses and injuries per 10,000 full-time employees (H) • Percent of farmers whose principal occupation is “farming” (SO) • Percent of farmland classified as “very large” (SO) • Percent farmland enrolled in conservation programs (EN) • Percent farmland treated with manure (EN) • Percent of farms classified as “very large” (SO) • Tons annual sheet and rill- (water) related soil erosion (EN) • Tons annual wind-related soil erosion (EN) • Value of agricultural sales (EC) • Value of chemicals purchased ($/acre) (EN) • Value of fertilizers, lime, soil conditions purchased ($/acre) (EN) • Value of government payments as percent of agricultural sales (EC) • Vegetable sales as percent of crop sales (EC)

Processing

• Annual payroll (ST) • Annual payroll as % of state payroll (ST) • Annual payroll as % of sector payroll (ST) • Average payroll per employee (ST) • Average payroll per employee as percent of state average payroll (ST) • Average payroll per employee as percent of US average payroll w/in sector (ST) • Employment as percent of state employment(ST) • Employment as percent of sector employment (ST) • Incidence of meat and poultry food recalls per manufacturing establishment (H) • Number of establishments (ST) • Number of illnesses and injuries per 10,000 full-time employees (H) • Number of paid employees (ST) • Payroll as percent of state payroll (ST) • Payroll as percent of sector payroll (ST) • Worker illnesses and injuries (H)

Distribution & Wholesaling

• Annual payroll (ST) • Annual payroll as % of state payroll (ST) • Annual payroll as % of sector payroll (ST) • Average payroll per employee (ST) • Average payroll per employee as percent of state average payroll (ST) • Average payroll per employee as percent of US average payroll w/in sector (ST) • Number of establishments (ST) • Number of illnesses and injuries per 10,000 full-time employees (H) • Number of paid employees (ST) • Payroll as percent of state payroll (ST)

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• Payroll as percent of sector payroll (ST) • Employment as percent of state employment (ST) • Employment as percent of sector employment (ST)

Retailing & Consumption

• Annual payroll (ST) • Annual payroll as % of state payroll (ST) • Annual payroll as % of sector payroll (ST) • Average payroll per employee (ST) • Average payroll per employee as percent of state average payroll (ST) • Average payroll per employee as percent of US average payroll w/in sector (ST) • Employment as percent of state employment (ST) • Employment as percent of sector employment (ST) • Convenience store sales as percent of retail food sales (EC) • Full service restaurant sales as percent of retail food sales (EC) • Grocery store sales as percent of retail food sales (EC) • Limited service restaurant sales as percent of retail food sales (EC) • Number of convenience stores per 10,000 people (SO) • Number of establishments (ST) • Number of grocery stores per 10,000 people (SO) • Number of full service restaurants per 10,000 people (SO) • Number of illnesses and injuries per 10,000 full-time employees (H) • Number of limited service restaurants per 10,000 people (SO) • Number of paid employees (ST) • Number of retail food establishments per 10,000 people (SO) • Number of special food services per 10,000 people (SO) • Number of supercenters/wholesale clubs per 10,000 people (SO) • Payroll as percent of state payroll (ST) • Payroll as percent of sector payroll (ST) • Percent adults w/diabetes (H) • Percent population obese (H) • Percent population overweight (H) • Percent households with food insecurity (low and very low) (H) • Percent population receiving Supplemental Nutrition Assistance Program (SNAP) benefits (SO) • Retail food sales as percent of state GDP (EC) • Special food service sales as percent of retail food sales (EC) • Supercenter/wholesale club sales as percent of retail food sales (EC)

Waste

• Annual payroll (ST) • Annual payroll as % of state payroll (ST) • Annual payroll as % of sector payroll (ST) • Average payroll per employee (ST) • Average payroll per employee as percent of state average payroll (ST) • Average payroll per employee as percent of US average payroll w/in sector (ST) • Employment as percent of state employment (ST)

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• Employment as percent of sector employment (ST) • Number of establishments (ST) • Number of illnesses and injuries per 10,000 full-time employees (H) • Number of paid employees (ST) • Payroll as percent of state payroll (ST) • Payroll as percent of sector payroll (ST) • Percent municipal solid waste recycled (EN)

Other “Wish List” Indicators Identified

The literature review and brainstorming process yielded an additional 145 indicators that ultimately were not included in the final study. The following indicators, while rejected because of data limitations and other reasons, may prove valuable in the future as data collection and food policy priorities evolve. Indicators are organized by food system activity. Performance measure indicators (structural (ST), economic (EC), environmental (EN), health (H), and social (SO)) are embedded within each activity.

Descriptive • Average age of general population (SO) • Average annual value of flood claims (EN) • Average farm size (EC) • Average length of growing season (number of days) (EN) • Average poverty rate (EC) • Average time (minutes) spent exercising each day/adult (H) • Average time (minutes) spent watching TV each day/adult (H) • Average time (minutes) spent exercising each day/child (H, SO) • Average time (minutes) spent watching TV each day/child(H, SO) • Average unemployment rate (EC) • Gross domestic product (EC) • Land use: average slope (EN) • Land use: soil organic matter (EN) • Land use: soil texture (EN) • Monthly Palmer Drought Severity Index (EN) • Number of hospitals/capita (SO) • Number of persons in poverty (EC) • Number of schools/capita (SO) • Number of self-renewing aquifers (EN) • Number of acres classified as wetland (Palustrine and Estruarine) (EN) • USDA hardiness zone (EN) • Voter participation rates (as a proxy for social awareness/decision-making) (SO) Input Supply • Net value added by industry establishments (EC) • Number of acres of irrigated farmland (EN) • Number of annual agriculture-related pesticide poisonings (H) • Value of gasoline, fuel, oil purchased for agricultural production (EC) • Value of industry sales (EC)

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Farming • Average years on present farm (SO) • Biodiversity: species richness (EN) • Cash rent paid for land, buildings, and grazing fees (aggregate $) (EC) • Cropland idle or used for cover crops or soil improvement but not harvested, pastured or grazed

(# farms, # acres) (EN) • Emissions from agricultural crops, N2O from agricultural soil management (EN) • Emissions from agricultural sources, N2O from manure management (EN) • Amount of excess nutrient run-off from CAFOs (EN) • Farms and acres classified as “limited resource” (SO) • Farms and acres classified as “non-family” (SO) • Irrigation expenses per acre using surface water (EC) • Irrigation expenses per acre using water from wells (EC) • Irrigated farm land (# farms, #acres) (EN) • Land use – cropland (EN) • Land use – permanent pasture (EN) • Land use – woodland (EN) • Land use – organic production (EN) • Mean annual wage for farm workers (SO) • Net rent received by non-operator landlords as a percent of agricultural sales (EC) • Net value added per farm (EC) • Number of bodies of impaired water (EN) • Number of days primary operator worked off-farm (E) • Number of farms marketing thru CSAs (SO) • Number of farms practicing MIG or rotational grazing (EN) • Number of farms selling direct to consumers (SO) • Number of farms selling value-added commodities (EC) • Number of miles of impaired water (EN) • Organic farm product sales (total $, avg. $/farm) (EN) • Percent of operators who reside on farm they operate (SO) • Production as a percent of food dietary guideline recommendations (H) • Rent and lease expenses for machinery, equipment, and farm share of vehicles (aggregate $)

(EC) • Sales from direct marketing (EC) • Value of chemicals used to control plant diseases (# farms, # acres) (EN) • Value of chemicals used to control weeds, brush, grass (# farms, # acres treated) (EN) • Value of contract farm labor expense (EC) • Value of custom work and custom hauling expenses (EC) • Value of farm interest expenses (EC) • Value of national flood insurance program claims paid per year (EC) Processing • Average cost of responding to food recalls, contamination (EC) • Gallons of bio-diesel production (EC, EN) • Gallons of ethanol production (EC, EN) • Incidence of fruit and vegetable food recalls (H)

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• Percent market share accounted for by top four companies in industry sector (e.g., grain processing, soy processing, meat processing) (EC)

• Net value added by industry establishments (EC) • Number of OSHA-exempt workers in the processing industry (SO) • Value of processing sales (EC)

Distribution & Wholesaling • Energy use associated with distribution (EN) • Net value added by industry establishments (EC) • Value of industry sales (EC) Retailing & Consumption • Average Body Mass Index (H) • Average Body Mass Index (BMI) of Supplemental Nutrition Assistance Program (SNAP)

participants/Average BMI of non-SNAP participants (H, SO) • Average calories consumed per person (H) • Average cost of implementing the Thrifty Food Plan per person (EC) • Average cost of treating diet-related disease per person (EC) • Average cost of treating food-contamination-related illness (EC) • Average household spending on food per week (EC) • Average monthly Emergency Food Assistance Program outlays/person (EC, SO) • Average monthly Farmers Market Nutrition benefit/recipient (EC, SO) • Average monthly Special Supplemental Nutrition Program for Women, Infants and Children

benefit/recipient (EC, SO) • Average pounds of fruit and vegetable consumption per person (H) • Average pounds of meat and poultry consumption per person (H) • Average pounds of grain and cereal consumption per person (H) • Average pounds of packaged sweets consumption per person (H) • Average pounds of prepared food consumption per person (H) • Average pounds of seafood consumption per person (H) • Average pounds of soft drink consumption per person (H) • Average pounds of solid fat consumption per person (H) • Average time (hours/day) spent preparing meals/household (SO) • Diet-related death rate (H) • Incidence of Type II diabetes among all persons (SO) • Incidence of Type II diabetes among children (SO) • Incidence of Type II diabetes among white and non-white populations (SO) • Measured drug resistance of salmonella and e-coli (H) • Net value added by industry establishments (EC) • National School Lunch Program participation rate (EC) • Number of farmers authorized to accept Farmers Market Nutrition Program coupons/number of

farms (EC, SO) • Number of food-borne diseases reported/person (Campylobactor, Listeria, Salmonela, Shingella,

STEC 0157, STEC non-015, Vibrio, Yersinia) (H) • Number of National School Lunch Program meals served (EC, SO) • Number of National School Lunch Program participants (EC, SO) • Number of schools that prohibit sales of junk food in school settings (SO)

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• Number of schools that prohibit sales of junk food for fundraising events (SO) • Number of schools that prohibit advertising for candy, fast food restaurants, or soft drinks on

school property (SO) • Number of schools that make fruits or vegetables and healthful beverages available to students

whenever other food or beverages are sold (SO) • Number of schools with a food service director (SO) • Number of Special Supplemental Nutrition Program for Women, Infants and Children recipients

(EC, SO) • Number of Supplemental Nutrition Assistance Program recipients (EC, SO) • Nutrient availability per person (H) • Percent of adults consuming more than five or more fruits and vegetables per day (H) • Percent of households preparing at least two meals/day (SO) • Percent of households with very low food security among children (H) • Percent of metro population consuming more than five or more fruits and vegetables per day

(H) • Percent of population receiving Farmers Market Nutrition Program benefits (EC, SO) • Percent of population receiving Special Supplemental Nutrition Program for Women, Infants and

Children benefits (EC, SO) • Percent of school-age children consuming less than five fruits and vegetables per day (H) • Percent of school-age children consuming at least one can of soda per day (H) • Percent of schools allowing school-age children to purchase high-fat foods from vending

machines (H) • Percent of schools offering low-fat a la carte foods (H) • Percent of Supplemental Nutrition Assistance Program participants (SO) • Prevalence of overweight and obesity – all persons, including children (H) • Rate of agricultural-related antibiotic resistance (H) • Special Supplemental Nutrition Program for Women, Infants and Children participation rate

(percent of eligible participants) (EC, SO) • State tax imposed on junk foods (SO) • Value of advertising for food and beverages (SO) • Value of advertising for limited service restaurants (SO) • Value of agricultural products sold direct (EC) • Value of annual Supplemental Nutrition Assistance Program (SNAP) outlays/US SNAP outlays

(EC, SO) • Value of Farmers Market Nutrition Program benefits/person (EC, SO) • Value of Special Supplemental Nutrition Program for Women, Infants and Children

benefits/person (EC, SO) • Value of retail coupon redemption (SO) • Value of industry sales (EC) Waste & Recovery • Average pounds of at-home consumer food and food packaging waste per person (SO) • Average pounds of food and food packaging waste from restaurants per establishment (SO) • Average pounds of retail food and food packaging waste per person (SO) • Average pounds of food and food packaging waste from special food services per establishment

(SO) • Food waste as a percent of total food disappearance (SO)

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• Food waste as a percent of total municipal solid waste (SO) • Glass bottle waste materials as a % of total municipal solid waste (EN) • Net value added by industry establishments (EC) • Plastic bottle waste materials as a % of total municipal solid waste (EN) • Percent of population participating in curbside recycling (EN) • Value of industry sales (EC)

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Appendix B: Indicator Definitions, Sources and Compilation Methods Food System Activities

Structural indicator data characterizing food system size and scope were collected for six activity sectors: input supply, farming, processing, distribution and wholesaling, retailing and waste and recovery. The majority of data come from the U.S. Census Bureau’s Economic Census and the USDA’s Agricultural Census, which uses SIC/NAICS codes to classify information from a wide range of industries. Economic Census data are collected every five years at national, state, and county levels. In some cases, Census data were incomplete due to non-reporting or because data were “withheld to avoid disclosing data of individual companies.” In these instances, data were imputed where possible to provide more complete data sets and to allow for comparisons of food system sector data across time and location. Input Supply: The input supply sector is represented by five SIC/NAICS code categories characterizing the production of farm inputs and the delivery of farm production services. SIC/NAICS codes included in this sector are: Support activities for animal production (1152), support activities for crop production (1151), pesticides, fertilizers and agricultural chemical manufacturing (3253), farm supply wholesalers (42291/424910), and farm machinery and equipment (333111). Crop-related support activities include custom services such as “soil preparation, planting and cultivating” as well as harvesting crews, crew leaders, and post-harvest activities. Livestock support activities include services related to raising animals such as breeding, boarding, spraying, and sheering. The majority of pesticides and other agricultural chemicals included in the above SIC/NAICS codes refer to those manufactured for fruit, vegetable, grain, and livestock production (although household pest control chemicals are included in this category). Fertilizer production includes manufactured synthetic fertilizers as well as compost. Farm supply wholesalers include those businesses that manufacture “agricultural and farm machinery and equipment” such as planting, tillage and harvesting equipment for grains, fruits, and vegetables; livestock housing and feeding equipment and sprayers. Detailed definitions of each SIC/NAICS code can be found in SIC/NAICS Code Definitions section of this appendix. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Farming: The farming sector is represented primarily by two NAICS codes reported in the Agricultural Census and account for crop and livestock production as well as production management services: Animal production (112), which includes aquaculture, and crop production (111). It is important to note that “crop production” figures do include grain used for biofuel production and therefore overstate the value of food-related production and sales within the farming sector. At the same time, wild-caught seafood data were not reported for the Census years studied and therefore are not reflected in the farm data. This may result in undervaluing food-related production and sales for some states. Employment and income measures for the farming sector are derived by aggregating farm operator and hired farm labor data from the Agricultural Census. Detailed definitions of each SIC/NAICS code and other measures can be found in SIC/NAICS Code Definitions section of this appendix. (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/.) Processing: The food processing sector is represented by 14 SIC/NAICS code categories covering the movement of raw inputs, the production of food and beverage manufacturing equipment as well as the manufacturing of foods and beverages, as well as the production of primary, secondary and tertiary packaging (containers designed to hold food, transport food products and facilitate shipping and

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handling). SIC/NAICS codes included in this food system activity are: Food manufacturing (311), beverage manufacturing (3121), folding paperboard manufacturing (322212), non-folding sanitary food container manufacturing (322215), all other converted paper manufacturing (322299), glass beverage container manufacturing (327213), food product equipment manufacturing (333294), automatic beverage vending machine manufacturing (333311), grain and field bean merchant wholesalers (42251/424510), livestock merchant wholesalers (42252/424520), other farm product raw material merchant wholesalers (42259/424590), specialized freight, local trucking (484220), specialized freight, long-distance trucking (484230), and farm product warehousing and storage (493130). Detailed definitions of each SIC/NAICS code can be found in SIC/NAICS Code Definitions section of this appendix. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Distribution & Wholesaling: The distribution and wholesaling sector is represented by two broad NAICS codes that describe the transport and sale of raw and processed products to the retail sector. The two NAICS codes used to define this sector are the “general line grocery merchant wholesalers” (4244) and the “beer, ale, wine and distilled alcoholic beverage wholesalers” (4248). These NAICS codes include a wide range of grocery products such as confectionary, fresh, frozen and processed fruits and vegetables, meats, breads, poultry, dairy, fish and seafood as well as beer, ale, wine and distilled alcoholic beverages. Detailed definitions of each NAICS code can be found in SIC/NAICS Code Definitions section of this appendix. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Retailing: The retailing sector is represented by ten NAICS code categories signifying the sale of food for at-home- (e.g., grocery stores) and food away-from-home consumption (e.g., restaurants). These categories include: Beer, wine and liquor stores (4453), convenience stores (445120), gas stations with convenience stores (447110), drinking places (7224), full-service restaurants (7221), limited service eating places (7222), special food services (7223), specialty food stores (4452), supermarkets and grocery stores (44511), and warehouse clubs and supercenters (452910). Detailed definitions of each NAICS code can be found in SIC/NAICS Code Definitions section of this appendix. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Waste & Recovery: Waste and waste recycling is an important component of the food system. The waste and recovery sector is represented by four NAICS codes that address municipal solid waste collection, management and, to some extent, recycling. These codes are: Landfills (562212), materials recovery (562920), septic systems (562991) and solid waste collection (562111). Municipal solid waste is defined as trash that includes packaging, food scraps, grass clippings, sofas, computers, tires and appliances. It does not include industrial, hazardous or construction waste. Detailed definitions of each NAICS code can be found in SIC/NAICS Code Definitions section of this appendix. (Sources: Environmental Protection Agency, “Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2009” and the U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Structural Indicators (Gray Indicators/Tan Maps) Annual Payroll ($1,000): Industry payroll data provide a measure of a sector’s relative labor costs and size. Non-farm payroll figures include “all forms of compensation, such as salaries, wages, commissions, dismissal pay, bonuses, vacation allowances, sick-leave pay, and employee contributions, to qualified pension plans paid during the year to all employees. For corporations, payroll includes amounts paid to officers and executives; for unincorporated businesses, it does not include profit or other compensation of proprietors or partners. Payroll is reported before deductions for social security, income tax, insurance, union dues, etc.” Retail foodservice sector payroll “includes tips and gratuities received by

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employees from patrons and reported to employers and excludes payrolls of departments or concessions operated by other companies at the establishment.” Farm sector payroll is calculated by aggregating net farm income from all farm operations and expenses for hired farm labor. All payroll figures are adjusted for inflation using the Consumer Price Index (not seasonally adjusted) with a 2007 base year. Payroll values were adjusted for inflation using the Consumer Price Index. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/; and the Bureau of Labor Statistics, Consumer Price Index, http://www.bls.gov/cpi/.) Annual Payroll as a Percent of State Annual Payroll: This indicator measures the economic contribution of food system payroll to aggregate state payroll and was created by dividing the annual payroll within a food system activity by the annual payroll for all economic sectors in the state. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Annual Payroll as a Percent of US Annual Payroll within the Sector: This indicator allows for the comparison of a state’s annual payroll within a food system activity to the national average payroll for the same food system activity. This indicator was created by dividing the annual payroll for a particular food system sector by the US annual payroll for the same sector. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Average Payroll: Average payroll per employee is a measure of relative labor costs for an activity sector. Average sector payroll per employee is calculated by dividing the inflation-adjusted annual payroll for all SIC/NAICS codes in a sector by the total number of paid employees for the same sector to arrive at an average annual payroll for full-time-equivalent employees in all food system sectors with the exception of the farming activity sector. Farming sector employment figures represent an aggregate of the number of “farm operators” and “hired farm laborors.” See “Annual Payroll” above for more information about farming sector data compilation. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/ and USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Average Payroll as a Percent of State Average: This measure allows for the economic comparison of food system sector payroll within a state to payroll for all other economic sectors in the state; it provides a relative average payroll value. This indicator was created by dividing the average payroll within a food system activity by the average payroll for all economic sectors in the state. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/. Average Payroll as a Percent of US Average Payroll within the Sector: This indicator measures a food system sector’s relative payroll cost and economic contribution within a state. Average payroll within a food system sector is compared to the US average payroll for the same food system sector. This measure allows for comparison of a state’s average payroll within a food system sector to average payroll within the same food system sector across the United States. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/. Land Area (million acres): Land area data are used to normalize “land in farms” and “farmland enrolled in conservation programs.” It measures public and private land area including agricultural acreage (cropland, grassland for pasture and range, grazed forest land, farmsteads and farm roads) and nonagricultural acreage (forest-use land, special uses land, urban land and other miscellaneous). (Sources: USDA, Economic Research Service, “Major Land Uses of Land in the US, 1997," http://www.ers.usda.gov/publications/sb973/sb973.pdf; USDA, Economic Research Service “Major Uses

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of Land in the United States, 2002,” http://www.ers.usda.gov/publications/eib14/ and USDA, Census of Agriculture, 2002 and 2007.) Land in Farms: This indicator measures changes in the amount of acreage primarily dedicated to agricultural production and conservation. “The acreage designated as ‘land in farms’ consists primarily of [owned and rented] agricultural land used for crops, pasture, or grazing. It also includes woodland and wasteland not actually under cultivation or used for pasture or grazing, provided it was part of the farm operator’s total operation. Large acreages of woodland or wasteland held for nonagricultural purposes were deleted from individual [census] reports during the edit process. Land in farms includes CRP, WRP, FWP, and CREP acres … For the 2007 census, operations with land enrolled in the CRP, WRP, FWP, or CREP were counted as farms, given they received $1,000 or more in government payments, even if they had no sales and otherwise lacked the potential to have $1,000 or more in sales. 2002 data may not include FWP or CREP acreage so data are not directly comparable.” (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/.) Number of Employees: The employment indicator measures the economic size of a food system sector and can be a predictor of economic growth or contraction within the sector. This indicator represents full-time equivalent (FTE) employment in all food system sectors, with the exception of farming, and include “salaried officers and executives of corporations. Included are employees on paid sick leave, paid holidays, and paid vacations; not included are proprietors and partners of unincorporated businesses.” Farming sector employment figures represent an aggregate of the number of “farm operators” and “hired farm labor.” Farm operators are defined as “the principal operator plus up to two additional operators. This may be fewer than the total operators on some farms.” Hired farm labor is defined in the Agricultural Census as “hired farm workers, including paid family members. Data exclude contract laborers.” Data on hired farm worker numbers may not be reliable because of large numbers of undocumented workers. Contract labor data is included in the input supply support activities. (Sources: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/; Guide to the 2002 Economic Census, Retail Trade, Appendix A: Explanation of Terms, Economic Census, U.S. Census Bureau, http://www.census.gov/econ/census/data/historical_data.html; and USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Number of Employees as a Percent of State Employment: This indicator provides an estimate of the economic and social contribution made by a food system sector to overall state employment. It is calculated by dividing the number of employees in a sector by the total number of employees in the state. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Number of Employees as a Percent of US Employment: This indicator provides a measure of one state’s contribution to national employment within a food system sector. It is calculated by dividing the number of employees in a sector by the total number of US employees in that sector. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Number of Establishments: This indicator measures economic growth or contraction in a food system sector. Establishment data for each sector is compiled by adding SIC/NAICS code data for businesses within a representative food system sector to arrive at an aggregate value. Establishment data for all food system sectors, with the exception of the farming sector, come from the Economic Census Bureau. An establishment, as defined by the Economic Census Bureau, is “a single location at which business is conducted … When two activities or more were carried on at a single location under a single ownership all activities generally were grouped together as a single establishment. The entire establishment was

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classified on the basis of its major activity and all data for it were included in that classification.” Establishment data for the farming sector comes from the Agricultural Census and is represented by the total number of farm operations. See “Number of Farms” for a definition of farm operations. (Sources: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/ and Guide to the 2002 Economic Census, Retail Trade, Appendix A: Explanation of Terms, U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/data/historical_data.html). Number of Farms: This figure is an indicator of farm sector growth and/or concentration. The Census of Agriculture defines a farm as “any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.” This figure includes all farm operations, ranches, nurseries, greenhouses, and aquaculture farms. (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Percent Land in Farms: This is a relative measure of land area dedicated to agricultural production and conservation. It is calculated by dividing farm land acreage by the total state acreage. For a definition of total state acreage, see “land area.” (Sources: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Population: This figure includes all people living in a state at the time of the U.S. Census and is used as a normalizing measure for two social indicators, the “percent of population receiving SNAP benefits” and the “number of retail food establishments per 10,000.” According the Census, “People were counted at their ‘usual residence’ … the place where the person lives and sleeps most of the time. This place is not necessarily the same as the person's voting residence or legal residence. Noncitizens who are living in the United States are included, regardless of their immigration status. Persons temporarily away from their usual residence, such as on vacation or on a business trip on Census Day, were counted at their usual residence. People who live at more than one residence during the week, month, or year were counted at the place where they live most of the year. People without a usual residence, however, were counted where they were staying on Census Day.” (Source: U.S. Census Bureau, “State and County Quick Facts,” http://quickfacts.census.gov/qfd/meta/long_POP010210.htm). Total Employment: The total employment figure is used to normalize food system sector employment in each state. It represents farm and non-farm employment for all business sectors as measured by the Agricultural Census and the Economic Census, respectively, excluding “rail transportation; National Postal Service; pension, health, welfare, and vacation funds; trusts, estates, and agency accounts; private households; and public administration” as well as “most establishments reporting government employees.” The total employment figure reported by the Economic Census was adjusted to include agricultural employment that is collected by the Agricultural Census. (Sources: U.S. Census Bureau, Economic Census, “State and County Business Patterns,” http://www.census.gov/econ/economywide.html and USDA, Agricultural Census, http://www.agcensus.usda.gov/). Total Payroll: This figure represents annual payroll for all business employees in a state as reported for the Agricultural Census and the Economic Census. Payroll includes “all forms of compensation, such as salaries, wages, commissions, dismissal pay, bonuses, vacation allowances, sick-leave pay, and employee contributions to qualified pension plans paid during the year to all employees. For corporations, payroll includes amounts paid to officers and executives; for unincorporated businesses, it does not include profit or other compensation of proprietors or partners. Payroll is reported before deductions for social security, income tax, insurance, union dues, etc. This definition of payroll is the same as that used by the

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Internal Revenue Service (IRS) on Form 941 as taxable Medicare Wages and Tips (even if not subject to income or FICA tax.” (Sources: U.S. Census Bureau, Economic Census, “State and County Business Patterns,” http://www.census.gov/econ/economywide.html and USDA, Agricultural Census, http://www.agcensus.usda.gov/). Economic Indicators (Blue Indicators and Maps) Ag R&D Expenditures/Ag Sales: This figure is an indicator of farm sector competitiveness and of the government’s commitment to innovation within the food system. It is calculated by dividing public agricultural research and development (R&D) expenditures by total agricultural sales. Expenditure data for agricultural R&D-related spending is compiled by the USDA’s Current Research Information System (CRIS) from data provided by USDA research agencies, State Agricultural Experiment Stations, 1890 Universities and Tuskegee University, Colleges of Veterinary Medicine and other cooperating institutions. (Source: USDA, National Institute of Food and Agriculture, CRIS Annual Funding Summaries, Table B, “Total Funds,” http://cris.nifa.usda.gov/fsummaries.html.) Farm Income Concentration: This figure is an indicator of the concentration of commodity production and farm income concentration within a state. It is calculated as the share (percent) of farm income derived from the three largest income-producing commodities in a state. Percentage figures were compiled using commodity sales data recorded by the USDA’s Economic Research Service. (Source: USDA, Economic Research Service, Farm Income Data Files, Cash Receipts, http://www.ers.usda.gov/Data/FarmIncome/FinfidmuXls.htm#receipts.) Government Payments as a Percent of Ag Sales: This figure represents public support for food production and is an indicator of farm income reliance on government subsidies. It is calculated by dividing the value of government payments paid to all farm operators by the value of agricultural commodity sales. Government payments, as measured by the Census of Agriculture, include direct payments made to farm operators for participation in conservation programs, direct and fixed payment programs, market deficiency payment programs, disaster payment programs and all other federal farm programs under which payments were made directly to farmers. Commodity Credit Corporation proceeds, state and local government agricultural program payments and federal crop insurance payments are not included. (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Net Farm Income as a Percent of Ag Sales: This figure measures the share of agricultural sales retained directly by farmers. It is calculated by dividing net farm income – the value of operators’ total revenue (fees for producing under a production contract, total sales not under a production contract, government payments, and farm-related income) minus total expenses paid – by the value of agricultural commodity sales. See also “value of ag sales.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Percent Livestock and Products: This figure is a gross indicator of livestock, poultry and livestock product supply and demand. It is calculated by dividing total sales of livestock, poultry and livestock products by the total value of agricultural commodity sales. Livestock and livestock products include cattle and calves; hogs and pigs; sheep and goats; horses, ponies, mules, burros, and donkeys; poultry and products from each of these animals, including aquaculture products. See also “value of ag sales” for the definition of “agricultural sales.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.)

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Percent Crops: This figure is a gross indicator of crop supply and demand. It is calculated by dividing aggregate crop sales by the value of agricultural commodity sales. Crops broadly include grains, oilseeds, dry beans, dry peas, tobacco, cotton and cottonseed, vegetables, melons, potatoes, sweet potatoes, fruits, tree nuts, berries, nursery, greenhouse, floriculture, sod, Christmas trees, short rotation woody crops and hay. See also “value of ag sales” for the definition of “agricultural sales.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Percent Fruits & Tree Nuts: This figure is a gross indicator of fruit, nut and berry supply and demand. It is calculated by dividing total sales of fruits, tree nuts, and berries by the total value of agricultural commodity sales. Fruits and tree nuts are defined as those crops that generally are not grown from seeds and have a perennial life cycle; they are included in “crop sales.” See also “value of ag sales” for the definition of “agricultural sales.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Percent Vegetables: This figure is a gross measure of vegetable supply and demand. It is calculated by dividing aggregate vegetable sales by the value of agricultural commodity sales. Vegetables broadly include all vegetables, melons, potatoes and sweet potatoes; they are included in “crop sales.” See also “value of ag sales” for the definition of “agricultural sales.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Retail Food Sales/State GDP: This figure is an indicator of a state’s economic retail activity; the retail sector’s contribution to the state’s overall economy. It is calculated by dividing retail food sales for a state by the state’s overall gross domestic product (GDP). Retail food sales data come from the Economic Census and is an aggregate of ten SIC/NAICS codes (see “Retailing” in Food System Sectors above for a listing of the SIC/NAICS codes included). For a definition of each SIC/NAICS code, see SIC/NAICS Code Definitions section of this appendix. State level GDP data measure value added for all industries within a state; “GDP is calculated as the sum of what consumers, businesses, and government spend on final goods and services, plus investment and net foreign trade … [It] is calculated as the sum of incomes earned by labor and capital and the costs incurred in the production of goods and services. That is, it includes the wages and salaries that workers earn, the income earned by individual or joint entrepreneurs as well as by corporations, and business taxes such as sales, property, and Federal excise taxes—that count as a business expense.” (Sources: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/ and Bureau of Economic Analysis, http://www.bea.gov/.) Share of Retail Food Sales (by retailer type): This figure is a gross indicator of household food consumption patterns at-home and away-from-home. Food sales are broken down by retail sector to allow analysis of an individual retail sector’s contribution to aggregate retail sales. Six of the retail sector categories identified as social indicators are listed: grocery stores, convenience stores, supercenters and wholesale clubs, limited service (fast food) restaurants, full service restaurants, and food services. When totaling sales from these sectors they will add to less than 100 percent because of the exclusion of beer, wine and liquor stores as well as drinking places. For a definition of each SIC/NAICS code, see “Retailing” definitions in SIC/NAICS Code Definitions section of this appendix. (Source: U.S. Census Bureau, Economic Census, 1997, 2002, 2007, http://www.census.gov/econ/census/.) Value of Ag Sales ($1,000): This figure is an indicator of the value of foodstuffs prior to processing or other value added activities. It represents the gross market value of agricultural products sold (less government payments) and has been adjusted for inflation using the Consumer Price Index with a 2007

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base year. (Sources: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/ and Bureau of Labor Statistics, Consumer Price Index, http://www.bls.gov/cpi/.) Environmental Indicators (Green Indicators and Maps) Percent Farmland Enrolled in Conservation Programs: This figure is an indicator of public support for farm-related conservation policies. It is calculated by dividing the number of acres of farm land enrolled in government conservation programs in a state by the state’s total farm land acreage. Government conservation programs included in the 1997, 2002, or 2007 census are the Conservation Reserve Program (CRP), Wetlands Reserve Program (WRP), Farmable Wetlands Program (FWP), and Conservation Reserve Enhancement Program (CREP). “CRP is a program established by the USDA in 1985 that takes land prone to erosion out of production for 10 to 15 years and devotes it to conservation uses. In return, farmers receive an annual rental payment for carrying out approved conservation practices on the conservation acreage. The WRP, FWP, and CREP programs are included under the CRP program that offers landowners financial incentives for conservation practices.” (Source: USDA, Census of Agriculture, 1997, 2002, 2007, http://www.agcensus.usda.gov/.) Percent Farmland Treated with Manure: Manure is an alternative to synthetic or other petroleum-based fertilizers reflecting a less-intensive farm management strategy. Indicator data for “farmland acres treated with manure” come from the Agricultural Census and first was collected in 2002. These data were normalized by total farmland acreage to provide an estimate of acreage that currently relies on manure for some level of fertility. Data are available for 2002 and 2007 Census years only. (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/About_the_Census/index.asp.) Percent Municipal Solid Waste Recycled: This figure is an indicator of consumer attitudes and behaviors toward waste and recycling. It measures the percent of municipal solid waste in a state that is recycled through curbside collection or composting (the remainder of waste is combusted or landfilled). Recycling data are reported by BioCycle Magazine. This indicators is compiled by dividing the tonnage of waste reported recycled by the total reported tonnage of municipal solid waste in a state. Municipal solid waste estimates are reported by most states and adjusted to exclude imported waste and include exported waste. Recycled waste includes all waste categories with the exception of construction and demolition debris and industrial waste. These waste categories are: glass, paper, C&D, steel, plastic, aluminum, other metals, wood, tires and organics (food and yard waste). Note: Comparable recycling rates are not available for 1997 due to a change in reporting methodology and due to insufficient data. Therefore, recycling indicator data are not reported for 1997. Moreover, the recycling of food -related organics is not counted at the state level by Biocycle, Columbia University or the Environmental Protection Agency – the three organizations primarily responsible for waste data collection. Therefore the recycling indicator may overestimate food-system waste. (Source: The State of Garbage in America, BioCycle Magazine, December 2008, http://www.jgpress.com/.) Tons of Sheet and Rill (Water)-Related Soil Erosion: Erosion is measured as the tonnage of sheet and rill-related soil erosion on cultivated and non-cultivated cropland. Erosion data estimates come from the National Resources Inventory (NRI) - a longitudinal sample conducted by the U.S. Department of Agriculture’s Natural Resources Conservation Service (NRCS) in cooperation with Iowa State University’s Center for Survey Statistics and Methodology. “Erosion rates are estimated average annual (or expected) rates based upon the cropping practices, management practices, and inherent resource conditions that occur at each NRI sample site. Climatic factors used in the erosion prediction equations (models) are based upon long-term average conditions and not upon one year's actual events … NRI estimates of sheet and rill erosion are based upon the standard Universal Soil Loss Equation (USLE).”

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(Source: USDA, Natural Resources Conservation Service, http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/nri/results/?&cid=stelprdb1041678). Tons of Wind-Related Soil Erosion: Wind-related soil erosion on cultivated and non-cultivated private lands is defined by the Natural Resources Conservation Service (NRCS) as “the breakdown, detachment, transport, and redistribution of soil particles by forces wind.” Erosion data estimates come from the National Resources Inventory (NRI) - a longitudinal sample conducted by the U.S. Department of Agriculture’s NRCS in cooperation with Iowa State University’s Center for Survey Statistics and Methodology. “Erosion rates are estimated average annual (or expected) rates based upon the cropping practices, management practices, and inherent resource conditions that occur at each NRI sample site. Climatic factors used in the erosion prediction equations (models) are based upon long-term average conditions and not upon one year's actual events … NRI estimates of sheet and rill erosion are based upon the standard Universal Soil Loss Equation (USLE).” (Sources: USDA, Natural Resources Conservation Services, National Resources Inventory, Soil Erosion on Cropland, National Resources Inventory, http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/nra/nri/ and USDA, Natural Resources Conservation Service, http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/landuse/crops/erosion.) Value of Chemicals Purchased/Acre: Farm chemicals represent one of the largest farm expense categories. Therefore this figure, when adjusted for inflation using the Producer Price Index, is a gross indicator of crop input intensity over time. It is calculated by dividing aggregate farm chemical expenses for a state by the state’s total farm acreage to arrive at an average farm chemical expense per acre. Chemicals expenses include insecticides, herbicides, fungicides, and other pesticides, including costs of custom application. (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/ and Bureau of Labor Statistics, Producer Price Index, 1997, 2002, 2007, 2012, http://www.bls.gov/ppi/.) Value of Fertilizers, Lime, Soil Conditioners Purchased/Acre: Farm fertilizers, lime and soil conditioners represent one of the largest farm expense categories. Therefore, this figure, when adjusted for inflation using the Producer Price Index, is a gross indicator of crop input intensity over time. It is calculated by dividing aggregate fertilizer, lime and soil conditioner expenses for a state’s farms by the state’s total farm acreage to arrive at an average fertilizer, lime and soil conditioner expense per acre. Fertilizer, lime and soil conditioner expenses include fertilizer and lime including rock phosphate and gypsum, and the costs of custom application. (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/ and Bureau of Labor Statistics, Producer Price Index, http://www.bls.gov/ppi/.) Health Indicators (Orange Indicators and Maps) Percent Adults with Diabetes: This indicator measures the prevalence of diabetes among the adult population. It is calculated as “Respondents aged >=18 years who report ever having physician-diagnosed [Type 1 or Type 2] diabetes … divided by the number of CDC respondents aged >=18 years who report or do not report ever having physician-diagnosed diabetes (excluding unknowns and refusals).” Diabetes is a life threatening disease “marked by high levels of blood glucose resulting from defects in insulin production, insulin action, or both.” There are three clinically-defined types of diabetes: Type 1, Type 2 and Gestational Diabetes. According to the Centers for Disease Control Type 2 diabetes is the most common form of the disease accounting for 90%–95% of all cases of diabetes. Overweight and obesity are leading risk factors of Type 2 diabetes. Data used for this indicator comes from the National Health Interview Survey (NHIS) of the CDC’s National Center for Health Statistics.

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(Source: Centers for Disease Control, http://www.cdc.gov/diabetes/statistics/prev/national/figageadult.htm.) Percent Households with Low or Very Low Food Security: This indicator measures household-level health and well-being using access to food throughout the year. It is calculated by the USDA’s Economic Research Service (ERS) from “responses to a series of questions about conditions and behaviors known to characterize households having difficulty meeting basic food needs. It measures the percent of all households within a state with one or more members who report having low or very low food security. According to ERS, food security, at a minimum, includes: 1) The ready availability of nutritionally adequate and safe foods; and 2) Assured ability to acquire acceptable foods in socially acceptable ways (that is, without resorting to emergency food supplies, scavenging, stealing, or other coping strategies). Households with “low” food security reportedly “reduced the quality, variety, and desirability of their diets, but the quantity of food intake and normal eating patterns were not substantially disrupted.” Households with “very low” food security reduced food intake “at times during the year” and “normal eating patterns [were] disrupted because the household lacked money and other resources for food.” ERS survey data come from the Current Population Survey: Food Security Supplement and represent three-year averages: 1996-98, 2002-04, and 2005-07. (Source: USDA, Economic Research Service, http://ers.usda.gov/publications/err66/err66b.pdf.) Percent Population Obese and Overweight: This figure is an indicator of change in a population’s overall health due to food consumption choices, among other things. Overweight and obesity are leading risk factors of Type 2 diabetes. Overweight and obesity both are “labels for ranges of weight that are greater than what is generally considered healthy for a given height … ranges of weight that have been shown to increase the likelihood of certain diseases and other health problems.” Overweight and obesity data used here are self-reported annually through state health department telephone interviews with U.S. adults and comes from the Center for Disease Control’s (CDC) Behavioral Risk Factor Surveillance System. The CDC defines overweight as an adult with a body mass index (BMI) between 25 and 29.9. Obesity in adults is defined as someone with a BMI greater than or equal to 30. The BMI – a ratio of weight and height – is used by CDC “because, for most people, it correlates with their amount of body fat.” Data for obesity rates in children are unavailable prior to 2003 and therefore is not included in the 1997 and 2002 data sets. (Source: Centers for Disease Control, Behavioral Risk Factor Surveillance System, http://www.cdc.gov/brfss/). Pounds of Meat and Poultry Recalled/Number of Meat and Poultry Manufacturing Establishments: This figure is an indicator of food safety for meat, poultry and processed egg products. Food recalls for these three product categories were normalized by the number of meat and poultry manufacturing plants (NAICS code #3116 for 1997, 2002, 2007 and codes #311611 and #311615 for 2012) to arrive at the total pounds of meat, poultry and egg products recalled annually on average for each manufacturing establishment in the state. According to FSIS, “Recalls are initiated by the manufacturer or distributor of the meat or poultry, sometimes at the request of FSIS. All recalls are voluntary.” Recall data are compiled by the USDA’s Food Safety and Inspection Service (FSIS) and come from the FSIS Recall Management Division based on manufacturer information. Note: This indicator is incomplete; it does not reflect recall data maintained by the Food and Drug Administration (FDA) for all other food products, including fruits and vegetables. FDA data are unavailable prior to 2009. (Source: USDA, Food Safety and Inspection Service, http://www.fsis.usda.gov/wps/portal/fsis/topics/recalls-and-public-health-alerts.) Worker Illnesses and Injuries (for Food System Sectors) per 10,000 Workers: This figure is an indicator of worker safety in the food industry. Nonfatal illnesses and injuries for full time workers (number and

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frequency with which they occur) are calculated as a percentage of full-time workers within each food system sector. Injury and illness data are reported by employers and collected by the Bureau of Labor Statistics (BLS) though the annual Survey of Occupational Injuries and Illnesses. Note: When calculating illness and injury incidents as a percentage of total employment within each food system sector, we attempted to match data from the BLS survey to the employment data from the Economic Census using SIC and NAICS codes. This task was difficult, however, for two reasons: 1) injury and illness incident data are limited or unavailable for some states (CO, ID, MS, NH, PA, SD, WV, WY); and 2) illness and injury data are reported at the state level for three- and four-digit SIC or NAICS codes while the SIC and NAICS codes identified for the food system sectors are represented by five and six digit subcategory codes. The injury and illness indicators were calculated for each state by summing data from available injury SIC and NAICS within each food system sector and dividing this by employment data for comparable SIC and NAICS codes. SIC codes represented by the 1997 and 2002 injury data are: 01,02,07,20, 265, 267, 287, 352, 495, 514, 54, and 58. NAICS codes represented by the 2007 injury data are: 111, 112, 115, 311, 312, 3222, 3253, 3272, 33311, 445, 562, and 722. (Sources: Bureau of Labor Statistics, Illness, Injuries, and Fatalities database, http://bls.gov/iif/oshstate.htm; and U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Social Indicators (Purple Indicators and Maps) Average Age of Farmers: This figure is an indicator of a state’s collective level of farming experience and has broad implications for land ownership, land management, succession planning, asset management and farm policy. Age data are collected through the Census of Agriculture. Age data were obtained from all farm operators in 1997 and up to three operators per farm in 2002 and 2007 census years. (USDA, Census of Agriculture, http://www.agcensus.usda.gov.) Number of Retail Food Establishments/10,000 People: This figure is an indicator of food availability and access to food among a state population. It is compiled by dividing the total number of establishments included in a state’s retail food sector by population data for the state. Estimates are provided for six of the retail establishment categories. These six categories – grocery stores, convenience stores, supercenters and wholesale clubs, limited service restaurants, full service restaurants, and special food services – were selected for the fact sheet based on discussions in food desert literature and USDA Food Atlas measures (http://www.ers.usda.gov/data-products/food-access-research-atlas/about-the-atlas.aspx). Category estimates were compiled using the SIC/NAICS codes listed in the “Retailing” sector definitions. Retail establishment data come from the Economic Census. Population data come from the U.S. Census Bureau. (Sources: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/ and U.S. Census Bureau, Population Estimates, http://www.census.gov/popest/index.html.) Percent Farmers Whose Principal Occupation Is Farming: This figure is an indicator of a state’s rural populace and of full-time farm sector employment. Included in this measure are farmers who self-report spending 50-percent or more of their work time at farming or ranching. This indicator is compiled and reported by the Census of Agriculture. (USDA, Census of Agriculture, http://www.agcensus.usda.gov.) Percent of Family Farms Classified as “Very Large”: This figure is an indicator of farm size, concentration, specialization and operating needs. It includes all family farms with gross sales of $500,000 or more. “Family farms” include all farms except those that are organized as corporations and cooperatives. Percent estimates were compiled using “very large” farm data from the Census of Agriculture and “land in farms” data from the National Agricultural Statistics Service (Sources: USDA National Agricultural Statistics Service, “Farms, Land in Farms, and Livestock Operations 2007

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Summary,” (1997). http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1521 and USDA, Census of Agriculture, http://www.agcensus.usda.gov.) Percent of Population Receiving SNAP Benefits: This figure is an indicator of food affordability and access; measuring the need for food assistance among all individuals (adults, teens and children). The Supplemental Nutrition Assistance Program (SNAP), formerly called the Food Stamp Program, accounts for the largest share of federal food assistance outlays annually. The program objective is to increase purchases of nutritious food among low-income populations. SNAP data are compiled by the USDA Food and Nutrition Service. Note: The percent of the population receiving SNAP benefits underestimates the actual need for food assistance as not all of those individuals who qualified for SNAP assistance utilized program benefits. (Source: USDA, Food and Nutrition Service, http://www.fns.usda.gov/fns/data.htm.) “Very Large” Farm Acreage as a Percent of Farm Acreage: See Very Large Farms. SIC/NAICS Code Definitions

Input Supply Support Activities for Animal Production (NAICS #1152): This industry comprises establishments “primarily engaged in performing support activities related to raising livestock (e.g., cattle, goats, hogs, horses, poultry, sheep). These establishments may perform one or more of the following: (1) breeding services for animals, including companion animals (e.g., cats, dogs, pet birds); (2) pedigree record services; (3) boarding horses; (4) dairy herd improvement activities; (5) livestock spraying and (6) sheep dipping and shearing.” (Source: Bureau of Labor Statistics, Quarterly Census of Employment, http://data.bls.gov/.) Support Activities for Crop Production (NAICS #1151): This industry comprises establishments “primarily engaged in performing services related to crop production. This industry subsector includes, among other things, the following services: crop planting, spraying, fertilizing and harvesting; fruit and vegetable sorting, grading and packing; farm management services; livestock breeding and other services and farm labor contracting and crew leadership services.” (Source: Bureau of Labor Statistics, Quarterly Census of Employment and Wages, http://www.bls.gov/cew/.) Pesticides, Fertilizers and Agricultural Chemicals (NAICS #3253): This industry group comprises establishments “primarily engaged in (1) manufacturing nitrogenous or phosphatic fertilizer materials; (2) manufacturing fertilizers from sewage or animal waste; (3) manufacturing nitrogenous or phosphatic materials and mixing with other ingredients into fertilizers; (4) mixing ingredients made elsewhere into fertilizers; and (5) establishments primarily engaged in the formulation and preparation of pesticides and other agricultural chemical manufacturing.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Farm Supply Wholesalers (NAICS #42291 in ’97; #424910 in ’02 and ‘07): This industry group comprises establishments “primarily engaged in wholesaling farm supplies, such as animal feeds, fertilizers, agricultural chemicals, pesticides, plant seeds, and plant bulbs.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Farm Machinery and Equipment Manufacturing (NAICS #333111): This industry group comprises establishments “primarily engaged in manufacturing agricultural and farm machinery and equipment, and other turf and grounds care equipment, including planting, harvesting, and grass mowing

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equipment (except lawn and garden-type).” Note: “Water supply and irrigation systems” (NAICS #221310) was not included in our measure of the “input supply” or “farming” activity sectors. This NAICS code accounts for water treatment plants and water supply sources such as pumping stations, aqueducts, and/or distribution mains. The data associated with water from these sources strictly for agricultural irrigation are not disaggregated from drinking water and water for other uses. Expense data for irrigated water are available through the Agriculture Census and is reported in our numbers. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Farming Animal Production (NAICS #112): This sector comprises establishments “primarily engaged in raising animals and harvesting fish and other animals from a farm or ranch. The establishments in this sector are often described as farms, ranches, dairies or hatcheries. A farm may consist of a single tract of land or a number of separate tracts, which may be held under different tenures. For example, one tract may be owned by the farm operator and another rented. It may be operated by the operator alone or with the assistance of members of the household or hired employees, or it may be operated by a partnership, corporation, or other type of organization. When a landowner has one or more tenants, renters, croppers, or managers, the land operated by each is considered a farm.” (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/.) Cropland Used Only for Pasture or Grazing. This category includes land used only for pasture or grazing which could have been used for crops without additional improvement. Also included was all cropland used for rotation pasture. However, cropland that was pastured before or after crops were harvested was to be included as harvested cropland rather than cropland for pasture or grazing. (Source: Census of Agriculture, http://www.agcensus.usda.gov/.) Crop Production (NAICS #111): This sector comprises establishments “primarily engaged in growing crops. The establishments in this sector are often described as farms, greenhouses, nurseries or orchards. A farm may consist of a single tract of land or a number of separate tracts which may be held under different tenures. For example, one tract may be owned by the farm operator and another rented. It may be operated by the operator alone or with the assistance of members of the household or hired employees, or it may be operated by a partnership, corporation, or other type of organization. When a landowner has one or more tenants, renters, croppers or managers, the land operated by each is considered a farm.” (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/.) Farm Operator: “The term operator designates a person who operates a farm, either doing the work or making day-to-day decisions about such things as planting, harvesting, feeding, and marketing. The operator may be the owner, a member of the owner’s household, a hired manager, a tenant, a renter, or a sharecropper.“ (Source: USDA, Census of Agriculture, http://www.agcensus.usda.gov/Publications/2007/index.asp.) Hired Farm Workers: The number of hired farm workers represents total farm labor, including family members. Notes: These figures are not available for 1997. (Source: USDA, U.S. Census of Agriculture, http://www.agcensus.usda.gov/Publications/2007/index.asp.) Processing All Other Converted Paper Product Manufacturing (NAICS #322299): This industry comprises establishments “primarily engaged in converting paper or paperboard into products (except containers, bags, coated and treated paper, stationery products, and sanitary paper products) or converting pulp

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into pulp products, such as egg cartons, food trays, and other food containers from molded pulp.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Beverage Containers, Glass, Manufacturing (NAICS #327213): This industry comprises establishments “primarily engaged in manufacturing glass packaging containers including beer bottles and other beverage containers; food packaging; jars for canning, bottling, and packaging; and soda bottles and containers.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Beverage Manufacturing (NAICS #3121 in ’97, ’02 and ’07; NAICS #31211-312140+311421+311511 in ‘12): This industry group comprises establishments “primarily engaged in manufacturing soft drinks, ice, and purifying and bottling water; manufacturing brewery products; winery products; and distillery products. Also included is (1) the artificially carbonating of water; (2) the brewing of beer, ale, malt liquors, and nonalcoholic beer; (3) growing of the grapes, and the manufacturing of wine and brandy, or making of wine or brandy from purchased materials, and the blending of wines and brandies; and (4) the distilling of potable liquors (except brandies) and the blending of liquors and other ingredients.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/ and U.S. Census Bureau, Annual Survey of Manufacturers, http://www.census.gov/programs-surveys/asm.html.) Distilling Equipment, Beverage, Manufacturing (NAICS #333294): This industry comprises establishments “primarily engaged in manufacturing food and beverage manufacturing-type machinery and equipment, such as dairy product plant machinery and equipment (e.g., homogenizers, pasteurizers, ice cream freezers), bakery machinery and equipment (e.g., dough mixers, bake ovens, pastry rolling machines), meat and poultry processing and preparation machinery, and other commercial food products machinery (e.g., slicers, choppers, and mixers).” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Farm Product Warehousing and Storage (NAICS #493130): This industry comprises establishments “primarily engaged in operating bulk farm product warehousing and storage facilities (except refrigerated). Grain elevators primarily engaged in storage are included in this industry.” (Source: Economic Census, U.S. Census Bureau, http://www.census.gov/econ/census/.) Folding Paperboard Box Manufacturing (NAICS #322212): This industry comprises establishments “primarily engaged in converting paperboard (except corrugated) into folding paperboard boxes without manufacturing paper and paperboard.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Food Manufacturing (NAICS #311): Industries in the food manufacturing subsector transform livestock and agricultural products into products for intermediate or final consumption. The industry groups are distinguished by the raw materials (generally of animal or vegetable origin) processed into food products. The food products manufactured in these establishments are typically sold to wholesalers or retailers for distribution to consumers, but establishments primarily engaged in retailing bakery and candy products made on the premises not for immediate consumption are included. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Grain & Field Bean Merchant Wholesalers (NAICS #42251 in ’97; NAICS #424510 in ’02 and ‘07): This industry comprises establishments “primarily engaged in the merchant wholesale distribution of grains, such as corn, wheat, oats, barley, and unpolished rice; dry beans; and soybeans and other inedible beans. Included in this industry are establishments primarily engaged in operating country or terminal

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grain elevators primarily for the purpose of wholesaling.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Automatic Beverage Vending Machines Manufacturing (NAICS #333311 in ’97, ’02 and ’07 and NAICS #333318 in ‘12): This industry comprises establishments “primarily engaged in (1) manufacturing coin, token, currency or magnetic card operated vending machines and/or (2) manufacturing coin operated mechanism for machines, such as vending machines, lockers, and laundry machines for cigarettes, ice cream, sodas, snacks, and postage stamps.” Food processing values are overestimated due to the inclusion of cigarette and postage stamp vending machines in this category. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Livestock Merchant Wholesalers (NAICS # 42252 in ’97; NAICS #424520 in ’02 and ‘07): This industry comprises establishments “primarily engaged in the merchant wholesale distribution of livestock (except horses and mules).” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Non-Folding Sanitary Food Container Manufacturing (NAICS #322215): This industry comprises establishments “primarily engaged in converting sanitary food board into food containers (except folding).” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Other Farm Product Raw Material Merchant Wholesalers (NAICS #42259 in ’97; NAICS #424590 in ’02 and ’07): This industry comprises establishments “primarily engaged in the merchant wholesale distribution of farm products (except grain and field beans, livestock, raw milk, live poultry, and fresh fruits and vegetables).” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Specialized Freight (Except Used Goods), Trucking, Local (NAICS #484220): This industry comprises establishments “primarily engaged in providing local, specialized trucking. Local trucking establishments provide trucking within a metropolitan area that may cross state lines. Generally the trips are same-day return. Includes hauling of ag products, farm products, milk, grain, livestock, and refrigerated products. This sector also includes non-ag product freight and, therefore, overstates the number of establishments, employees, sales and payroll associated with the transportation of ag and food products.” Note: We attempted to obtain an estimate of the share of non-ag-related freight for this sector so as to adjust the data accordingly. This estimate, however, was unavailable from the Census Bureau. (Source U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Specialized Freight (Except Used Goods), Trucking, Long-Distance (NAICS #484230): This industry comprises establishments “primarily engaged in providing long-distance specialized trucking. These establishments provide trucking between metropolitan areas that may cross North American country borders. Includes hauling of ag products, farm products, milk, grain, livestock, and refrigerated products. This sector also includes non-ag product freight and, therefore, overstates the number of establishments, employees, sales and payroll associated with the transportation of ag and food products.” Note: We attempted to obtain an estimate of the share of non-ag-related freight for this sector so as to adjust the data accordingly. This estimate, however, was unavailable from the Census Bureau. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.)

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Distribution & Wholesaling Grocery and Related Product Wholesalers (NAICS #4224 in ’97; NAICS #4244 in ’02 and ’07): This industry group comprises establishments “primarily engaged in the merchant wholesale distribution of (1) general-line groceries; (2) packaged frozen food; (3) dairy products (except dried or canned); (4) poultry and poultry products; (5) confectioneries; (6) fish and seafood (except canned or packaged frozen); (7) meats and meat products; (8) fresh fruits and vegetables; and (9) other grocery and related products.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Retailing Beer, Wine and Liquor Stores (NAICS #4453): This industry group comprises establishments “primarily engaged in retailing packaged alcoholic beverages, such as ale, beer, wine, and liquor.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Convenience Stores (NAICS #445120): This industry comprises establishments known as convenience stores or food marts (except those with fuel pumps) “primarily engaged in retailing a limited line of goods that generally includes milk, bread, soda, and snacks.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Drinking Places (NAICS #7224): This industry group comprises establishments “primarily engaged in preparing and serving alcoholic beverages for immediate consumption.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Full-Service Eating Places (NAICS #7221): This industry group comprises establishments “primarily engaged in providing food services to patrons who order and are served while seated (i.e., waiter/waitress service) and pay after eating. Establishments that provide this type of food service to patrons with any combination of other services, such as take-out services, are classified in this industry.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Gas Stations with Convenience Stores (NAICS # 447110): This industry group is comprised of “establishments engaged in retailing automotive fuels (e.g., diesel fuel, gasohol, and gasoline) in combination with convenience store or food mart items. These establishments can either be in a convenience store (i.e., food mart) setting or a gasoline station setting. These establishments may also provide automotive repair services.” Sales data were adjusted using a conversion factor representing packaged beverages, prepared food, and beer. The conversion factor was determined by looking at in-store category sales reported by the National Association of Convenience Stores and from annual Food Industry Review reports. Conversion factors for 1997, 2002, 2007 and 2012 were 0.464, 0.376, 0.292, and .147, respectively (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Limited-Service Eating Places (NAICS #7222): This industry group comprises establishments “primarily engaged in providing food services where patrons generally order or select items and pay before eating. Most establishments do not have waiter/waitress service, but some provide limited service, such as cooking to order (i.e., per special request), bringing food to seated customers, or providing off-site delivery.” (Source: Economic Census, U.S. Census Bureau, http://www.census.gov/econ/census/.) Special Food Stores and Services (NAICS #4452, 722): This industry group comprises establishments “primarily engaged in retailing specialized lines of food such as meat, fish and seafood, baked good, confectionary items and nuts” and in providing food services at one or more of the following locations:

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(1) the customer's location; (2) a location designated by the customer; or (3) from motorized vehicles or non-motorized carts.” Establishments include institutional vendors at hospitals and schools as well as private vendors at convention centers and airports, as well as street vendors. (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Supermarkets and Other Grocery (Except Convenience) Stores (NAICS #445110): This industry group comprises establishments generally known as supermarkets and grocery stores that are “primarily engaged in retailing general line food, such as canned and frozen foods; fresh fruits and vegetables; and fresh and prepared meats, fish, and poultry. Included in this industry are delicatessen-type establishments.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Warehouse Clubs and Supercenters (NAICS #452910): This industry comprises establishments “known as warehouse clubs, superstores or supercenters primarily engaged in retailing a general line of groceries in combination with general lines of new merchandise, such as apparel, furniture, appliances, tobacco, home-based care items, and other non-food services.” Sales, employee, and payroll data were adjusted using a conversion factor representing packaged dry groceries, perishable groceries, soft drinks, candy, snacks, beer, wine and liquor. The conversion factor was determined by looking at in-store category sales reported in “Food Industry Review” reports by the Food Institute. Conversion factors for 1997, 2002, 2007 and 2012 were 0.456, 0.411, 0.353 and .525, respectively. (Source: Food Institute, Food Industry Review, 2008, 2003, 1998; and U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Note: We do not include “Other Direct Selling Establishments” (NAICS #454390) in the retail sector. Other direct selling establishments are defined as: “establishments primarily engaged in retailing merchandise (except food for immediate consumption and fuel) via direct sale to the customer by means, such as in-house sales (i.e., party plan merchandising), truck or wagon sales, and portable stalls (i.e., street vendors).” These include temporary fruit stands, frozen food providers, bottled water providers, door-to-door sellers as well as “coffee-break service providers,” Christmas tree sellers, cigarette stands and direct selling of retail merchandise. Waste Materials Recovery Facilities (NAICS #562920): This industry comprises establishments “primarily engaged in (1) operating facilities for separating and sorting recyclable materials from nonhazardous waste streams (i.e., garbage) and/or (2) operating facilities where commingled recyclable materials, such as paper, plastics, used beverage cans, and metals, are sorted into distinct categories.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Septic Tank & Related Services (NAICS # 562991): This industry comprises establishments “primarily engaged in (1) pumping (i.e., cleaning) septic tanks and cesspools and/or (2) renting and/or servicing portable toilets.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Solid Waste Collection (NAICS #562111): This industry comprises establishments “primarily engaged in one or more of the following: (1) collecting and/or hauling nonhazardous solid waste (i.e., garbage) within a local area; (2) operating nonhazardous solid waste transfer stations; and (3) collecting and/or hauling mixed recyclable materials within a local area.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.)

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Solid Waste Landfills (NAICS #562212): This industry comprises establishments “primarily engaged in (1) operating landfills for the disposal of nonhazardous solid waste or (2) the combined activity of collecting and/or hauling nonhazardous waste materials within a local area and operating landfills for the disposal of nonhazardous solid waste.” (Source: U.S. Census Bureau, Economic Census, http://www.census.gov/econ/census/.) Compilation Methods Consumer Price Index (CPI): “The CPI and its components are used to adjust other economic series for price changes and to translate these series into inflation-free dollars. Examples of series adjusted by the CPI include retail sales, hourly and weekly earnings, and components of the National Income and Product Accounts. An interesting example is the use of the CPI as a deflator of the value of the consumer's dollar to find its purchasing power. The purchasing power of the consumer's dollar measures the change in the value to the consumer of goods and services that a dollar will buy at different dates. In other words, as prices increase, the purchasing power of the consumer's dollar declines.” Producer Price Index (PPI): The PPI “measures the average change over time in the selling prices received by domestic producers for their output. The prices included in the PPI are from the first commercial transaction for many products and some services.” The PPI industry series #PCU3253-3253 was applied to the “value of chemicals purchased” and the “value of fertilizers, lime, and soil conditioners purchased.” State Gross Domestic Product (GDP): Gross domestic product is measured for individual states “as the expenditures of households on goods and services plus business investment, government expenditures, and net exports.” Data are collected and assembled by the Bureau of Economic Analysis (BEA) from Federal and state and local government agencies and bureaus, other BEA accounts, and private companies. All industries are included in the GDP estimate. (Source: Bureau of Economic Analysis, http://www.bea.gov/iTable/iTable.cfm?reqid=70&step=1&isuri=1&acrdn=1.) Weighting: Weights were imputed for Economic Census employment, wage, and sales data when necessary. Weights were developed using value ranges provided by the Economic Census. Midpoint values were estimated using the ranges. Weights were established by first dividing midpoint values by the sum of the midpoint values. This weight was then multiplied by the sum of missing values to arrive at a weighted average for the missing data point.

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Appendix C: Principal Components Analysis of State Level Food System Indicators9

The suite of food system indicators developed under this project is large and multifaceted. Also, many of the indicators may be highly correlated with each other. The indicators would be much easier to use if they could be reduced to a smaller number of composite “scores” that maintain as much as possible of the information captured by the entire set of indicators. Principal Component Analysis (PCA) is a commonly used statistical technique for data reduction and exploratory structure analysis. PCA groups variables in a way that highlights their similarities and differences, while losing as little of the original information as possible. Each original variable is given a particular weight and loaded onto a component. The component represents a particular attribute, which is defined by the types of variables that load on it. In the context of this project, the very high dimensionality of the food system indicators can be reduced using PCA. A lower dimensionality facilitates comparison between states and over time. With indicator data for multiple years, testing the stability of component structures over time is also important. Common Principal Component Analysis (CPCA) is a statistical technique used to test the level of similarity of component structures between different PCA groups. CPCA uses the component weightings for each year determined through PCA and determines if they are statistically different from one another. If they are shown to not be statistically different, the component structure is stable over time. This allows for multiple years to be pooled for analysis, and a single component structure to be used for component score production. Using a single component structure for component score production allows for direct comparison of component scores over time. This appendix summarizes methods and findings from Benjamin Scharadin’s M.S. thesis, Principal Component Analysis of State Level Food System Indicators (Scharadin 2012). The overall objective of the thesis was to explore the potential for effective data reduction in the set of state level food system indicators for all 50 states and to test the stability of the component structure over time. The thesis had the following three specific objectives:

1. Reduce the dimensionality of the state level indicators over multiple years, while exploring if there are interpretable and meaningful underlying component structures to the indicators.

2. Compare the structures over time to test for similarity. If the structures are shown to be stable, data for new time periods can be described using the current structures.

3. Compare the states across regions and over time using the structure found. In the sections that follow, we first present a brief overview of PCA and CPCA methods. We then describe the analysis conducted in the thesis and summarize key findings.

9 This appendix was prepared by Robert King. It is based on Benjamin Scharadin’s M.S. thesis, Principal Component Analysis of State Level Food System Indicators (Scharadin 2012) and does not include data from 2012. The complete thesis is available online on the project web site or at http://purl.umn.edu/123113. Much of the text in this appendix is taken directly from the thesis.

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PCA Methods

Application of PCA is made quite simple by statistical packages such as SAS and Stata, however there are still numerous issues needed to be addressed for PCA to produce meaningful results. First, proper management of the data before the PCA process begins is necessary to ensure proper analysis. This includes the standardization of variables and the removal of outliers from the analysis. Second, careful consideration must be given to each of the following decisions in the PCA process: addressing the number of observations needed to be included, appropriateness of the included variables, the number of components to retain, and the rotation method. Scharadin (2012) consulted a number of sources in order to consider the large amount of variation in PCA procedures (Jackson 1991, Joliffe 2002, Garson 2009, Hardle and Simar 2007). Number of Observations: Methodologists differ widely in opinion on the question of the number of observations needed to perform PCA accurately. The most popular and most often followed recommendations are based on a subject to variable ratio. These recommendations state that the ratio of observations to variables should be at least between 5 and 10 (Bryant and Yarnold 1995). In this project there are 50 subjects, each state being a subject. Therefore, this rule, which is among the least restrictive in the literature, implies that PCA can only be conducted on a maximum of 10 variables at a time. Correlation Matrix Considerations: The correlation matrix can be used as a preliminary measure of the appropriateness of each variable’s inclusion in a group. The correlation matrix shows the Pearson-correlation coefficient between each variable. Since this is a measure of how related the variables are, a variable that is appropriate for the PCA group should have some high positive correlation coefficients with other variables in the group. If a variable has only low correlation with other variables under consideration, there is a large possibility it will not load strongly on any one component. Therefore, the variable should not be included in the group because it will not allow for adequate data reduction and clean component loadings. Covariance Matrix and Standardization of the Data: Variables measured on different scales in the data analyzed by PCA can cause inaccurate component structures (Jackson 1991, Hardle and Simar 2007). This issue must be carefully considered and rectified before the techniques are performed. If the variables are allowed to remain in differing scales, the assumption of homogeneous variances for PCA will be violated, since heterogeneous scaling of the variables is a major cause of heterogeneous variances. Greater weight is given to the variables with larger variances, and with differing scales larger variance may be due solely to a larger scale (Jackson 1991). Standardizing the scale of each variable by subtracting the mean from each observation and then dividing by the standard deviation results in equal weight being given to each variable. This guarantees that the homogeneous variances assumption holds. All data used in the PCA techniques in this study were standardized. Outliers: Inaccurate component structure can be caused by outliers being included in the PCA (Hardle and Simar 2007). The large variance associated with outliers can detract from the underlying variance of the majority of the data. If the outliers remain in the data, the variance of each variable may be significantly different, from the variance of each variable without the outliers. In practice this can cause variables to load together on a component because each has large outliers. It may also cause a variable with a truly low variance to load with variables of high variance. There are techniques available that weight the data to allow for the outliers to be included in the analysis. In this study outliers were defined as having standardized values greater than 2.5 or less than -2.5. These values were removed from the data before PCA was performed.

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Determining the Number of Components: There are numerous criteria for determining the number of components to retain in the PCA. Many references detail the multiple criteria available to determine the number of components, but three criteria are commonly used in current PCA research because of their ease of use and accurate results when considered in conjunction with each other: the Guttman-Kaiser criterion, the scree plot, and the variance explained criterion (Joliffe 2002, Garson 2009, Hardle and Simar 2007). These are reviewed by Scharadin (2012, pp. 30-33). A combination of scree plot analysis and the Guttman-Kaiser criterion was used in this study to determine the number of retained components. Rotation Method: Rotation of components is designed to obtain simple structure of the resulting components. When the structure is simple, each variable will only identify strongly with one component (Garson 2009). This means that variables should have a loading close to one on one component and loadings close to 0 on the other components. In practice simple structure is hard to obtain, but rotation methods allow for the components to be close. With a simpler structure, the components are more easily interpreted because the loadings are clearly on one component. There are two groups of rotation methods with multiple types in each group (Jackson 1991, Hardle and Simar 2007). The first group is orthogonal rotation methods, where the components remain uncorrelated with each other. The second group is oblique rotations methods, which allow for the components to be correlated with each other. Orthogonal rotation methods are most commonly used. Varimax rotation (Garson 2009) is the most popular of these, and it was used in this study. CPCA Methods

Common Principal Component Analysis is a statistical technique used to confirm or deny a level of similarity between the component structures of two groups (Flury 1988, Schott 1988, Schott 1999). The CPCA process analyzes similarities among the covariance matrices of the different groups. There is a hierarchy of similarity and a maximum likelihood estimate can be found for each hierarchy level (Flury 1988). CPCA begins at the third level of the hierarchy, followed by a restricted case of Partial CPCA. The hierarchy of similarity ranges from equality to no statistical similarity.

1. The highest level of similarity between covariance matrices is equality. This implies that the covariance matrix of k groups are all equal.

2. The second highest level of similarity is proportionality between covariance matrices. This implies that the covariance matrices of k groups are just proportional to a single covariance matrix by some set of constants.

3. The third highest level of similarity is the CPCA model. This model implies that the covariance matrices of k groups produce the same characteristic roots or components.

4. The fourth highest level of similarity is the partial CPCA model. This model implies that the covariance matrices of k groups are similar up to q components and the rest may be specific to each matrix, where q is less than p and p is the total number of components.

5. The final and lowest level of similarity is arbitrary covariance matrices. Each covariance matrix is independent of the others and must be analyzed on its own.

Partial CPCA is the fourth level of the similarity hierarchy and allows for a certain number, q, of the first components of covariance matrices to be common and for the remainder, p-q, to be specific to each covariance matrix (Flury 1988). This type of analysis is appropriate when trying to compare component structures over multiple groups, while only retaining the first q components. The last p-q components are discarded for similar reasons discussed earlier in the overview of PCA. If CPCA is conducted rather

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than partial CPCA the hypothesis of similar component structure may be rejected due to components that would not to be retained anyway. If a particular model of partial CPCA fails to be rejected, pooling the data for a PCA is appropriate as long as only the first q components are being retained. For example, if a partial CPCA model comparing the first three PC’s is not rejected, the group’s variables can be pooled and one PCA run (Schott 1999, Flury 1988). The first three component structures of the pooled PCA will be similar to each individual PCA, but they will be of a higher quality because the number of observations will have been significantly increased. In addition, the component structure can be considered stable over time, when the groups are multiple years. Analysis and Results

In order to investigate the potential for achieving significant data reduction through the use of PCA methods, Scharadin (2012) conducted PCA and partial CPCA for three groups of state level indicators:

1. Economic Structure of the Food System Group, which included: percent of total state employment in input supply, percent of total state employment in primary production, percent of total state employment in processing, percent of total state employment in distribution, percent of total state employment in retail, percent of total state employment in waste and recovery, percent of total state land in farms, percent of total state population in metropolitan areas, and number of grocery stores per 10,000 people.10

2. Agricultural Production Intensity Group, which included: value of chemicals purchased per acre; value of fertilizer, lime, and soil conditioners purchased per acre; percent of farmland enrolled in conservation programs; percent of agricultural land that is irrigated; percent of agricultural land used for crops; net farm income as a percent of agricultural sales; government payments as a percent of agricultural sales; percent of agricultural sales from crops; and percent of farms classified as “very large.”11

3. Health and Consumption Group, which included: percent of adult population who are obese,

percent of adults with diabetes, percent of population eligible for SNAP benefits, percent of food expenditures in grocery stores or supercenters, percent of food expenditures in convenience stores, percent of food expenditures in full service restaurants, percent of food expenditures in limited service restaurants, and percent of food expenditures in food service establishments.12

These groups of indicators were chosen from among many possible groupings after giving consideration to the importance, relatedness, and meaningfulness of the indicators included in the group. In light of the subjects to variables ratio requirement discussed earlier, no group includes more than ten variables. For each group of variables, a PCA was performed for data from each year in order to test the component structure of the years independently. Partial CPCA was then used to do a formal comparison

10 The percent of total state population in metropolitan areas was not included in the final set of indicators. 11 The percent of agricultural land that is irrigated and the percent of agricultural land used for crops were not included in the final set of indicators. 12 Definitions for the obesity and SNAP indicators differ slightly from those for indicators included in the final set of indicators.

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across years for each indicator group. The covariance matrices were compared using a CPCA program written by Patrick Phillips (Phillips 2000). The years were then pooled and a PCA was conducted to the level found appropriate by partial CPCA. The component structure for the pooled PCA was then used to calculate component scores for each year. Component scores for each component and year were then compared for four states within each U.S. Census region. The states selected for the Northeast Region were New Jersey, New York, Pennsylvania, and Vermont. The states selected for the Midwest Region were Indiana, Iowa, Minnesota, and Nebraska. The states selected for the South Region were Arkansas, Mississippi, Texas, and Virginia. The states selected for the West Region were Arizona, California, Colorado, and Oregon. Economic Structure of the Food System

Data on percent of state population living in metro areas were not available for 1997. Therefore, the analysis for the Economic Structure of the Food System group of indicators could only be conducted using data from 2002 and 2007. The year-by-year PCA for this group of variables identified three components:

i. Upstream production activities, which included percent of total state employment in input supply, percent of total state employment in primary production, percent of total state employment in processing, and percent of total state land in farms.

ii. Retail, which included percent of total state employment in distribution, percent of total state employment in retail, and number of grocery stores per 10,000 people.

iii. Waste, which included percent of total state employment in waste and recovery and percent of total state population in metropolitan areas.

The partial CPCA results indicated that the component structure for this group is very stable over time, and state-by-state component scores did not exhibit clear trends between 2002 and 2007. Regional comparisons of component scores showed that populous states in the Northeast were weighted heavily towards retailing, while those for agricultural states in the Midwest were weighted toward primary production. Agricultural Production Intensity

The year-by-year PCA for the Agricultural Production Intensity group of indicators also identified three components:

i. Crop input intensity, which included value of chemicals purchased per acre; value of fertilizer, lime, and soil conditioners purchased per acre; percent of agricultural land used for crops; and percent of agricultural sales from crops.

ii. Government payments and conservation, which included government payments as a percent of agricultural sales, percent of farmland enrolled in conservation programs, and percent of farms classified as “very large.”

iii. Irrigated farming, which included percent of agricultural land that is irrigated and net farm income as a percent of agricultural sales.

The partial CPCA results indicated that the component structure for this group also is very stable over time. State-by-state component score for crop input intensity and irrigated farming did not exhibit clear trends between 1997 and 2007. However, most states sowed an increase in the government payments and conservation component score between 1997 and 2007. Regional comparisons of component

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scores showed that government payment and conservation scores were highest in Midwestern and southern states. Health and Consumption

The year-by-year PCA for the Health and Consumption group of indicators identified only one consistent and interpretable component: “health and nutrition,” which included percent of adult population who are obese, percent of adults with diabetes, percent of population eligible for SNAP benefits, and percent of food expenditures in limited service restaurants. Two other components were identified for each year, but these did not have any clear interpretation and included different variables from year to year. Give these results, it was not surprising that the partial CPCA indicated that the data could be pooled across years only for a single component, health and nutrition. This component showed a strong positive trend between 1997 and 2007. An increase in the score for this component would generally be associated with unfavorable changes – i.e., increases in obesity, diabetes, and SNAP eligibility. Concluding Remarks

The results of this analysis show that it is possible to achieve meaningful data reduction, but multiple complications can occur during the process. The first major complication is that the component structure is very sensitive to the variables that are included in a PCA. With 50 subjects, the subjects to variable ratio requires no more than ten variables be included in a PCA at one time. Consequently, many group combinations must be analyzed, possibly differing by only one variable, before a meaningful and interpretable component structure is achieved. In addition to the procedural complications caused by the subjects to variable ratio, there is a conceptual complication. The holistic nature of the food system is a main reason for the complexity of the indicators set. However, by separating the indicator set into groups for the PCA analysis the holistic nature is decreased. It is possible for meaningful connections to be missed because of limits on the number of variables that can be included in a single PCA. A second major complicating factor is partially shown by the Health and Consumption Group. Although each year’s component structure for a single group may have meaningful interpretations, they may not be consistent over time. Food system comparisons across time cannot be made with varying component structures. The component scores are a summed product of the standardized data value and the variables component loading. Any changes in component scores are attributable to changes in the data value if a single component structure is used across all years. However, if the component structure changes over time the component scores are not comparable because the change in the score may be due to the data value, the variables component loading, or likely both. Consequently, only groups that are shown to have consistent component structures over time can be used to compare the food system over time. Despite its limitations, the requirement to have consistent component structures over time has a major benefit. With the component structures shown to be stable over time, the pooled component structure can be used with future indicator sets to create component scores without performing the entire analysis. This allows for comparisons of food systems to continue using fewer resources. In addition, a known consistent set of indicators can be collected, eliminating the lengthy task of creating the indicator list itself.

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The ability to compare aspects of the food system through component scores can help to inform policy decisions in multiple ways. The most basic way is by using the component scores to paint a picture of the current food system. A few scores can allow a policy maker to assess how one state compares with other states. In this way, particular issues can be highlighted and addressed quickly. The component scores also allow for states to assess policy effectiveness over time. A policy is put in place with a particular expected outcome. Tracking and comparing the component scores over time will allow policy analysts to see if the intended outcome was achieved, was not achieved, or had a different outcome than expected. In addition, if similar policies are enacted in multiple states or a federal policy is enacted for all states, the component scores allow for a comparison across states and across time to analyze the relative effectiveness of the policy. The differences in the states can then be studied to address the differences in the policy’s outcomes. References

Bryant and Yarnold. 1995. “Principal Component Analysis and Exploratory and Confirmatory Factor Analysis.” American Psychology Association Books.

Flury, Bernhard. 1988. Common Principal Components and Related Multivariate Models. 1st ed. New York: Wiley and Sons Inc.

Garson, G. David. 2009. Factor Analysis: Statnote. 5th ed. North Carolina State Publishing. (http://tx.liberal.ntu.edu.tw/~PurpleWoo/Literature/!DataAnalysis/Factor%20Analysis-types.htm)

Hardle, Wolfgang and Leopold Simar. 2007. “Applied Multivariate Statistical Analysis.” Second Edition. Berlin: Springer, 215-269.

Jackson, J. Edward. 1991. “A User's Guide to Principal Component Analysis” New York: Wiley and Sons: Wiley-Interscience.

Joliffe, I. T. 2002. “Principal Component Analysis.” 2nd ed. New York: Springer Statistics. Phillips, Patrick. 2000. “CPC – Common Principal Component Analysis Program.” University of Oregon.

(http://pages.uoregon.edu/pphil/programs/cpc/cpc.htm) Scharadin, Benjamin P. 2012. Principal Component Analysis of State Level Food System Indicators. M.S.

Thesis, Department of Applied Economics, University of Minnesota. (http://purl.umn.edu/123113)

Schott, James R. 1999. “Partial Common Principal Component Subspaces.” Biometrika 86.4 899-908. Schott, James R. 1988. “Partial Common Principal Component Subspaces in Two Groups.” Biometrika

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