AGRICULTURAL & APPLIED ECONOMICS

University of Wisconsin-Madison Department of Agricultural & Applied Economics

Staff Paper No. 572

February 2014

Poverty, Public Health and Local Foods

By

Steven C. Deller, Laura Brown and Amber Canto

__________________________________

AGRICULTURAL & APPLIED ECONOMICS ____________________________

STAFF PAPER SERIES

Copyright © 2014 Steven C. Deller, Laura Brown & Amber Canto. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

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POVERTY, PUBLIC HEALTH AND LOCAL FOODS Steven Deller Department of Agricultural and Applied Economics 515 Taylor Hall – 427 Lorch St University of Wisconsin-Madison Madison, WI 56706 [email protected]

Laura E. Brown Center for Community Economic Development University of Wisconsin-Extension

Amber Canto Family Living Programs University of Wisconsin-Extension

Abstract In this exploratory analysis we explore the interplay between poverty, public health and access to local foods using data for U.S. counties. We ask one simple question: Does access to local foods dampen or mitigate the relationship between poverty and health? As expected we find a strong relationship between poverty and public health and we also find that access to higher levels of local foods activity is associated with higher levels of public health. The interaction between poverty and local foods, however, suggests that higher concentrations of both are associated with poorer, not better health. From a global perspective we find that the presence of local foods related activity tends to have a positive impact on health, but that relationship is not consistent across the United States. Our results suggest that the interplay between local foods, poverty and health is subtle and the resulting policy implications may make sense in some parts of the United States but not in others.

An earlier version of this paper was presented the Federal Reserve Bank of Atlanta workshop on poverty, December 2013.

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POVERTY, PUBLIC HEALTH AND LOCAL FOODS Introduction The links between poverty and poor health outcomes are numerous, complex and, as expressed by Murray (2006: p923) “inextricably intertwined.” This relationship has long been documented in literature (Mansfield and Novick 2012; Montgomery, Kiely and Pappas 1996; Pappas, Queen, Hadden and Fisher 1993) and serves as a focal point of significant public policy discussions, most notably since Lyndon Johnson’s call for a “War on Poverty” in 1964 launching a new era of welfare legislation (Harrington 1962; Losa, Antonovsky and Zola 1969). For the purposes of this research we consider three broad health issues related to poverty: (a) lack of access to health care; (2) unhealthy living environments; and (3) lack of access to nutritional foods. Policy measures addressing lack of access to health care are generally advanced on two levels; lack of health insurance and limited geographic access to health care services. Indeed, an underlying motivation of the Patient Protection and Affordable Care Act of 2010 is to expand health care access to all Americans, particularly the working poor who may not qualify for Medicaid, do not have employer provided health insurance or cannot afford health insurance. The relationship between income or poverty and unhealthy living environments has been extensively examined within a literature often referred to as the Environmental Kuznut’s Curve (for example, see Dasgupta, Laplante, Wang and Wheeler 2002; Dietz, Rosa and York 2012; Dinda 2004 and Keene and Deller (forthcoming)). According to this theory, for some types of pollutants the indicators of environmental quality will decline until a region reaches a particular average income. At this level of income the income-environmental trade-off switches direction and environmental quality increases. Thus, regions with a lower average income are willing to accept poorer environmental conditions associated with some types of economic activity (e.g., mining or dirty manufacturing) and the potentially negative health consequences in the hope of higher levels of income. The third line of reasoning that links poverty and poor health centers on access to nutritional foods (e.g., Link and Phelan 1996; Olson 1999; Darmon and Drewnowski 2008). While many of the studies in this line of work are orientated at the microeconomic level (i.e., using data on individuals through census derived data or surveys) there is a growing literature that takes on more of an ecological approach including consideration for neighborhood or community characteristics. In this case researchers focus on food access in low income communities. Most recently this has been discussed within the structure of “food deserts” (e.g., Sadler, Gilliland and Arku 2012). While this term was first used in studies of food access in poor urban areas of Scotland in the 1990s, it has come into widespread use throughout the literature which seeks to better understand the poverty, health and food relationships (e.g., Moreland, Diez-Rous and Wings 2006; Wang, Kim, Gonzales, MacLeod and Winkleby 2007). While there are numerous definitions of what constitutes a “food desert” and ways of quantitatively assessing whether or not a particular community or neighborhood is a food desert, (Donald 2013; Bader, Purciel, Yousefzadeh and Neckerman 2010) the central premise is that people 2|Page

living in a food desert do not have access to affordable healthy food. This can include lack of access to supermarkets, a standard measure of a community being a food desert, or access to unhealthy foods such as those offered at convenience stores or inexpensive fast-food restaurants. Sadler, Gilliland and Arku (2012) observe that exposure to only unhealthy foods result in the development of poor eating habits, particularly among youth. A natural policy solution offered to combat the poverty and poor health relationships within the context of access to affordable healthy food is to encourage supermarkets to move into these communities or neighborhoods. Increasingly, researchers and communities are looking to farmers markets, one face of the “local foods movement,” as a strategy to address food deserts (e.g., Garnett 2013). Creamer and Dunning (2012) suggest that farmers markets are only one small piece of the “local foods solution” to food deserts and notions of community supported agriculture (CSA), farm-to-school programs and neighborhood gardens should be part of the discussion. One significant problem with this literature is that there is little existing evidence that local foods are a viable solution or part of a solution to the poverty and poor health problem. Several studies have, however, found positive impacts of farmers markets. In a study of perceived food deserts in London, Ontario Canada Larsen and Gilliland (2009) argued that the introduction of one farmers market solved the community’s food desert status. Along similar lines, Webber, Stephenson, Mayes and Stephenson’s (2013) study of farmers market patrons in Kentucky found that a sample of these patrons had a higher understanding of nutrition than the typical Kentuckian. As a result, they suggest, more farmers markets should be promoted. In contrast, Donald (2013: p232) notes that most of this literature takes on a supply-side focus evolving “the old adage ‘if you build it they will come’, or in this case, ‘if you build it they will eat better’.” This approach fails to address the demand side of the equation. That is, do low-income people demand or want the types of foods that are offered through local foods type institutions such as farmers markets and community supported agricultural (CSA) farms? While Pelletier and her colleagues (2013) are seeking to understand the demand side, the research is still far from conclusive (e.g., Zepeda and Li 2007). Another significant question is if local food systems can be scaled up sufficiently to address the broader problem. In a study of Flint, Michigan, Sadler, Gilliland and Arku (2012) found that the operation of farmers markets three days of the week was not sufficient to address the access to quality food issue. They note that the “cash only” nature of the farmers market coupled with a limited range of food offerings including basic staples limits the ability of farmers markets to truly address issues of food deserts. An additional challenge noted in the literature includes the limited timeframe of most farmers markets that general operate once per week. In a series of essays Born, Brown and Purcell (Born and Purcell 2006; Brown and Purcell 2005; Purcell and Brown 2005) warn of what they call the “local trap.” They define the “trap” as the tendency of food activists and researchers to assume that there is something good inherent about the local scale. If globalization is “bad” from a social or economic justice and/or environmental sustainability perspective then the anti-thesis of globalization which is local must be “good.” Within the local foods literature the industrialization and globalization of food (large scale monoculture farming, multinational food corporations including fast food companies) is a primary driving factor behind food deserts and poor food choices provided to low-income communities and neighborhoods. The danger of the “local 3|Page

trap” within this literature is the tendency to draw conclusions sympathetic to local foods that are not supported by existing research. The intent of the research reported here is to take an objective view in the analysis of the poverty, health and local foods relationship. Specifically, we ask if access to the type of agricultural activity commonly associated with local foods affects the link between poverty and poor health. To accomplish this task we follow the lead of Salois (2012) who found that higher concentrations of farms with direct sales for human consumption and farmers markets tends to have a dampening impact of the rate of obesity and/or diabetes. Like Salois we use U.S. county level data to model public health. Unlike Salois we pay particular attention to the spatial relationships within the data. As described in detail below, county level data do not necessarily reflect the true spatial boundaries of the relevant markets or the supply of local food related production. Beyond these introductory comments the paper is composed of four sections. In the next section we outline our modeling framework where we address how we will go about answering our basic question. We then provide a more descriptive analysis of the data followed by an outline of the spatial estimators that we use to formally operationalize our simple model. We then discuss our results and close the paper with a discussion of the implications of our work, its limitations and future research directions.

Modeling Framework The basic question that we are seeking to better understand is: does access to the type of agricultural activity commonly associated with local foods affect the link between poverty and poor health? To empirically estimate these relationships we constructed quantitative measures of both health and local foods. Conceptualizing an empirical model of comprehensive local food systems and public health metrics within the limitations of existing and appropriate data sources presented a significant challenge. Below we outline our strategy and justification for creation of the indices used in our model. The logic of our modeling framework can be visualized via Figure 1. While the health literature is awash with empirical studies seeking to better understand the factors that influence health outcomes, for our analysis we suggest that there are four major categories: (1) social capital, (2) education, (3) access to health care, and (4) poverty. The role of local foods impacts can occur in two ways: (1) directly (β5) in terms of access to healthier foods and (2) indirectly (β6) by helping weakening the poverty and health relationship. The direct relationship between local foods and health (β5) could be bi-directional. Within the food desert framework creating a supply of local foods creates access to potentially healthier foods and thus higher levels of health as outlined in Figure 1. It is also possible that healthier people may create a market for local foods which would reverse the direction of causation outlined in Figure 1.

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The indirect effect works through the poverty and health relationship and is not subject to reverse causation. If we find that higher levels of local foods dampens the poverty and health relationship (i.e., β6 > 0) then there is evidence supporting the strategy of promoting local foods in low income areas. If higher concentrations of local foods does not dampen the poverty and health relationship then using local foods as a strategy to promote health in low-income areas becomes questionable. Education, access to health care and poverty are all widely recognized as contributors to health. In addition to the growing interest in access to foods, whether in the context of food deserts or local foods, the role of social capital in understanding health outcome is gaining wider attention (Cattell 2001; Szreter and Woolcock 2004). Ziersch and her colleagues (2005) notes that the social capital and health relationship, particularly within a community or ecological setting, has received more attention, but the linkage is less than definitive. Specifically inconsistencies in the measurement of both social capital and health, as well as conceptual and empirical approaches, have led to inconsistencies in empirical results. Thus for our modeling we control for social capital. There are several ways to estimate the relationships outlined in Figure 1 such as path analysis which can employ a range of methods to estimate the directional relationships to classical regression analysis. For the analysis reported in this paper we elect to estimate a non-linear regression model which can be expressed as:

(

)

Here Poverty is the overall personal poverty rate, Social Capital is an index developed by Rupasingha, Goetz and Freshwater (2006), Rupasingha and Goetz (2007) and Goetz and Rupasingha (2006), Health

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Care Access is simply the number of general practitioner physicians per 1,000 persons1 Of particular interest to this analysis is the impact of the local foods and poverty interaction term (Local Foods * Poverty), specifically the parameter β6. The next step in the analysis is to define what we mean by public health and local foods. As mentioned earlier, we build on Salois (2012) work that includes two measures of health (rates of obesity and diabetes) and three measures of local foods (percent of farms with direct sales for human consumption, direct farm sales for human consumption per capita, and farmers markets density), plus a range of other control variables including some traditional metrics of food deserts. We use principal component analysis to combine these different health and local food related characteristics into two separate indices. Principal component derived measures are, in essence, linear combinations of the original variables where the linear weights are the eigenvectors of the correlation matrix between the set of factor variables. Each factor is constructed orthogonal to the others. In other words, principal component is a mechanical method of inspecting the sample data for directions of variability and using this information to reduce a collection of variables into a single measure. Ideally, the final measure captures the essence of the original collection of variables. While the pros and cons of principal component analysis are well known, and a range of alternative approaches are available, we suggest that the approach used here may help to advance discussions about how to best empirically analyze these factors.

Defining the “Local Foods” Index In the vast food systems literature there is significant variation in terms of how local foods are defined. This has led to difficulties in both defining and measuring local food systems (Martinez et al 2010). Durham, et al., (2009), for example, found that many consumers disagree that food produced beyond a 100-mile designation could be considered local foods. In a study of food retailers Dunne, et al. (2011) found that grocers’ perceptions of local food varied significantly from one another. Martinez and his colleagues (2010) find that opinions of what defines local also varied by the agricultural product being considered. In our review of existing literature we found that local foods is characterized: from the consumer or intermediated consumer perspective (Dunne, 2010, Zapeda, 2006), in terms of proximity- distance, drive time, food-miles (Dunne, 2010, Darby 2008, King, 2010, Zapeda, 2006), by geo-political boundaries such as states (Darby, 2008), by ownership structure (local or non-local) of farm (Low, 2011), relationship to place (Marsden, 2000), production techniques used, marketing channels used (Low, 2011), size/scale, (Low, 2011), products grown, quality

1

The social capital index includes religious, civic, social, business, political, professional, labor organizations, bowling centers, physical fitness facilities, public golf courses and sport clubs, managers, and promoters concentrations (number of organizations/associations per 10,000 population), voter turnout, Census response rate and number of non-profit organizations without including those with an international approach. The social capital index is created using principal component analysis with the first principal component considered the index of social capital. The technical documentation and data for the index is available at: http://aese.psu.edu/nercrd/community/tools/social-capital

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relationships /supply chain (Marsden, 2000, King, 2010), or integration of the related supply chain (Marsden, 2000). Our choice of local foods metrics was based on agricultural characteristics related to local food systems as defined in Low and Vogel (2011). Data regarding “direct sales for human consumption” serves cornerstone for our measurement however the index is comprised of geographic, production, market and economic factors. In a related analysis of farmers market performance Schmidt and Gomez use similar metrics in the creation of an empirical model of food environment that includes local food systems factors (2011). As described in our literature review, these factors are consistent with a food environment model that considers the following characteristics of agriculture as one component of the overall food environment. These data are drawn from the USDA Food Environment Atlas [1] and the USDA 2007 Census of Agriculture. We use eight local foods metrics:        

Number of Farms with Sales less than $100K per 1,000 Population Number of Organic Farms per 1,000 Population Number of Farms with Value Added Production per 1,000 Population Number of CSA Farms per 1,000 Population Number of Orchard, Fruit, Vegetable and Berry Farms per 1,000 Population Number of Farms with Direct Sales per 1,000 Population Direct Sales per Capita Number of Farmers' Markets per 1,000 Population.

The “Number of Farms with Direct Sales per 1K Population 2007” and “Sales Per Capita, Direct Sales 2007” metrics represent the number and sales of farms in a county that sell directly to consumers including sales from roadside stands, farmers markets, pick-your-own, door-to-door, among others. It does not include sales of craft items or processed products, such as jellies, sausages, and hams. It is important to recognize that while farms with direct sales may be located in a particular county; their primary market area may be outside of the county. The measures of "Number of Orchard, Fruit, Vegetable and Berry Farms per 1K Population 2007” are data from the US Census of agriculture based on types of products produced by the farm. Low and Vogel (2011) found that of all farm selling local foods, local food sales account for 65% of sales for fruit, vegetable and nut farms on average and only 37% for livestock farms. Vegetable fruit and nut farms also accounted for 40% of all farmers using direct to consumer marketing channels exclusively and 60% of all farms using for direct to consumer and intermediated marketing channels. While the study additionally notes farms participating in local (direct or intermediated markets) operate fewer acres and grow higher value commodities, this metric was sufficient to represent the overall profile of these farms in our index. The “Number Farmers' Markets per 1K Population” is anther metric of markets in the food environment and is compiled by the Agricultural marketing Service. A farmer's market is a retail outlet in which two or more vendors sell agricultural products directly to customers through a common 7|Page

marketing channel and at least 51 percent of retail sales are direct to consumers. As mentioned before, farmers markets has become the “face” of the local foods movement. A complement to farmers markets are community support farms (CSAs) where consumers buy shares of a farms products which can be harvested by the consumer at the farm themselves or take delivery of products at a predetermined location. Many farms that are part of a local foods system also tend to be organic. While there is a debate within the industry as to whether or not USDA organic certification is a good thing or not and not all organic farms are part of the local foods system, we suggest that organic operations tend to a common characteristics of farms producing for the local foods market. Low and Vogel (2011) also found that the predominance of farms that sell for direct human consumption, whether that is through farmers markets, CSAs, or other local outlets tend to be of modest size. Thus we include the “Number of Farms with Sales less than $100K per 1K Population” to reflect the tendency of these farms to be of smaller size. If there is a higher concentration of these smaller farms, the likelihood of there being a stronger local foods oriented market is higher. Our final metric to be included in the local foods index is “Number of Farms with Value Added Production per 1K Population”. Many farmers that sell into the local foods markets have modest value added production activities associated with their farms ranging from jellies, sausages and prepared meats to dried fruits and pickled vegetables. Unfortunately, the Census of Agriculture reports out a simple “yes/no” as to whether or not value added production is present on the farm, scale of that production, if present, is not reported. Clearly additional elements could be added to the index, such as small scale (micro-enterprise) food processors, certain types of livestock producers, or intermediated sales. An area of future work would include the robustness of the overall results across alternative measures of local food systems. We believe, however, that counties that have higher levels of these characteristics will be more closely aligned with local foods activity.

Developing the “Public Health” Index Given the complexity of public health issues and limitations of available data, developing a comprehensive metric of public health offers a similar challenge. This study builds on the work of the University of Wisconsin Population Health Institute who, in partnership with the Robert Wood Johnson Foundation, developed the County Health Rankings and Roadmaps program which allows for the construction of detailed population health profiles for every county in the US (University of Wisconsin Population Health Institute, 2013). The County Health Rankings are based on a model of population health that attempts to account for the many factors that influence health: physical environment, social and economic factors, clinical care, and health behaviors. Together, these factors contribute to overall health outcomes (University of Wisconsin Population Health Institute, 2013). For this exploratory analysis we elected to use five measures of ‘public health’ encompassing indicators of both health factors and health outcomes, tied closely to diet and nutrition.

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Health Factors:

Health Outcomes:

• •

• • •

Percent Adult Obese Percent Adult Diabetic

Percent Low Birth Weight Percent Fair/Poor Health Years of Potential Life Lost (Premature Death)

Low birth weight is an indicator of current and future morbidity, as well as premature mortality risk, serving as both a maternal and infant health indicator. With regards to maternal health, low birth weight can serve as a proxy for maternal health risk, accounting for both environmental risk as well as modifiable health behaviors. Bailey and colleagues (2007) showed that low birth weight was associated with suboptimal nutrition and maternal overweight. Additional studies have shown that low birth weight and weight status interact later in life leading to increased risk for cardiovascular disease (RichEdwards, et al, 2005, Irving, et al, 2000). Percent of the population reporting fair/poor health is a selfreported indicator of overall quality of life. Compared to those with normal body-mass-index, overweight and obese adults were more likely to report their health as fair or poor (Mokdad, et al, 2001). The measure years of potential life lost (YPLL) attempts to quantify the number of years of life lost due to premature death, defined by a standard cutoff age in a population (Vila, et al, 2006). Heart disease, cancer, stroke, and diabetes, are among the top ten leading causes of death in the US (CDC, 2012). Danaei and colleagues (2009) further substantiate the relative risk of death attributable to chronic disease via estimates suggesting that a number of modifiable risk factors were found to be directly attributable to cardiovascular disease (low fruit and vegetable intake), cancer (overweightobesity, low fruit and vegetable intake), and diabetes (overweight-obesity) death in both males and females. Higher values of each of these metrics in the public health index are associated with poorer overall levels of health. One of the limitations to the public health data is that the data are not available for all counties for every year. This means that data values may be missing for smaller, more rural counties. The selection of these particular metrics could be viewed as arbitrary as minimizing the problem of missing data was a consideration. Given the multifaceted influences on health outcomes, and dietary behavior, we then constructed indices for our control metrics: demographics, access to health care, and access to food (other than local foods). The selection of these components is in line with the County Health Rankings Framework. The results of this analysis are provided in Table 1.2 The final health index accounts for 64.6 percent of the variation in the five individual health metrics. It is important to keep in mind that higher values of the health index are associated with higher levels of “poor” health. This will be important when interpreting the empirical modeling results below. The local food index explains 46.2 percent of the variation in the eight original local food characteristics. One of the limitations to principal component analysis is that as more variables are entered into the analysis, the explanatory power of the final index tends to be reduced. This is because one index tries to capture potentially higher levels of 2

Principal component analysis is sensitive to variable scaling and we standardize all variables to mean zero and unit standard deviations.

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variation across individual variables. Note for the local food index the concentration of farms with direct sales and farms with value added activity tend to drive the final index. Farmers market concentration tends to have little influence in the final index. This means that our index is less focused on farmers markets which has historical been the “face” of the local foods movement and the focus of most studies looking at the role of local foods in access to food (i.e., food deserts).

Table 1: Public Health and Local Food Indices Low Birth Weight Rate Adult Obesity Rate Adult Diabetes Rate Premature Death (Years of Potential Life Lost) Poor or Fair Health (%)

Eigenvalues 0.3764 0.4383 0.4887 0.4746 0.4497 Variance Explained 0.6462

Number of Farms with Sales less than $100K per 1,000 Population Number of Organic Farms per 1,000 Population Number of Farms with Value Added Production per 1,000 Population Number of CSA Farms per 1,000 Population Number of Orchard, Fruit, Vegetable and Berry Farms per 1,000 Population Number of Farms with Direct Sales per 1,000 Population Direct Sales per Capita Number of Farmers' Markets per 1,000 Population Variance Explained

0.3995 0.3324 0.4309 0.3614 0.2597 0.4585 0.3173 0.1878 0.4624

Descriptive Analysis Before turning to the formal modeling consider a simple mapping of the key variables: the health (Map 1) and local foods (Map 2) indices and poverty (Map 3).3 In addition to a simple mapping we also estimate and map the Getis-Ord Gi* spatial clustering statistic (Getis and Ord 1992; Ord and Getis 1995). The Gi* statistic is a spatial modeling method that identifies patterns of spatial significance. In other words, are the spatial patterns that seem to “appear” in the mapping of the variable of interest statistically significant? Within economic geography the Gi* statistic is used to identify spatial “hot” and “cold” spots in the data. Recalling that higher values of the health index are associated with poor levels of health it becomes clear that there are strong concentrations of poor health (hot spots) in the southern states particularly in the Louisiana-Mississippi-Alabama region and parts of the Appalachia region. One can also “pick out” certain Native American reservation area in the western states and northern Great Plains. The also clearly identified spatial clusters of low values of the health index (cold spots) that are in 3

Any “holes” or “gaps” in the maps is a result of missing data generally from the data used to generate the health index. If data were missing for any one variable, the county is removed from the analysis.

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parts of the northeastern region of the U.S., the upper Midwest, parts of the Rocky Mountains, and regions along the Pacific Coast. There is nothing surprising in these patterns. The mapping of the overall poverty rate (Map 2) reveals familiar patterns: poverty clusters appear in the southern states and parts of Appalachia, areas associated with some Native American reservations, and the Texas border with Mexico. A visual comparison of the hot and cold spots of the health index and poverty suggest strong overlaps. A mapping of the local foods index identifies several statistically significant hot and cold spots. Our local foods index tends to be supply or production focused (farm concentrations) as opposed to demand or consumer focused (e.g., farmers markets). First, there are large cold spots with low levels of the local foods index throughout the south-eastern U.S. up the Atlantic Coast and a smaller ban from Illinois to northern Ohio. There are also clear hot spots including parts of northern New England, a small pocket in Kentucky-Tennessee, the Pacific Northwest including parts of Montana. The hot spot in parts of Wisconsin and Minnesota is not surprising but the large area of the Great Plains is somewhat unexpected. Because our measures of farms associated with local food production per 1,000 persons, the size of these farms coupled with very low population densities may explain the pattern. For example, in some more remote Great Plains counties there may be a small number of large farms with very low populations. If these large farms set aside a few acres for local foods production (i.e., direct sales for human consumption) the combination of factors may make these counties appear to be part of a local foods hot spot. As with a visual comparison of the health index and poverty hot and cold spots, there appears to be some commonalities with the hot and cold spots of our local foods index. To explore the relationship between our key variables we estimate a series of correlation matrices (Pearson, Spearman and Kendall) along with a set of factor analyzes (Alpha, Harris and Principal Components). The correlation matrices are provided in Table 2 while the factor analysis is provided in Table 3. When looking at these simple statistical analysis we must recall that higher values of the health index is associated with poorer levels of health. As such the direction of relationships (sign of the coefficients) outline in Figure 1 is reversed. Given this interpretation of the health index, we find that the data generally supports all of our general hypotheses: poverty is strongly tied to poorer public health, while the presence of local foods activities, education, access to health care, and higher levels of social capital are all related with higher levels of public health. The strongest relationship in terms of the strength of the statistical correlations is poverty and education while the local foods measure has the weakest relationship. A simple scatterplot of poverty on our health index (Figure 2) reveals a strong and weakly nonlinear relationship: higher poverty rates are associated with higher values of our health index which by construction means higher poverty is associated with worse health. The strong positive relationship tappers off slightly at higher poverty rates. A simple scatterplot of our local foods index on health reveals a fainter relationship, but it is inverse suggesting that higher values of the local food index is associated with lower levels of our health index which means higher levels of health. This latter scatterplot does reveal that the local foods index does not have a normal distribution is skewed to the left suggesting that there are a higher proportion of counties that have little local foods activity, as we have defined it (Table 1) relative to those counties that have higher levels. 11 | P a g e

The relationship between poverty and the local foods index, however, is consistently insignificant. This could be interpreted a couple different ways. First, low income people may not demand the type of foods associated with local foods. This speaks to the argument within the foods desert literature that lower income people prefer fast or convenient foods and the market or demand for local foods is simply not present in poorer areas. Second, the demand is not sufficient to justify the suppliers of local foods to enter into poor markets. This would be a supply response that tends to form the backbone of the logic underpinning the bulk of the food desert literature. There are weak inverse relationships between our local foods index and education and access to health care. This is likely due to the tendency of local food production to be located in more rural counties which tend to have lower education and health care access levels. Not surprisingly, there is an inverse relationship between poverty education, health care access and social capital. Turning to the factor analysis (Table 3) three variables contribute the most to the total variation of the set of variables is the public health index, poverty and education. From the spatial mapping and simple one-to-one correlation analysis it is clear that poverty and health move very closely together and education is closely tied to both health and poverty when compared to access to health care, social capital and local foods. Of the six variables within the analysis our measure of local foods has the weakest statistical association with the other variables. An important variable in our analysis as outlined in the regression model is poverty times our local food index ( ) or parameter relationship β6 in Figure 1. Again, the central question is if higher concentration of local foods related activity dampens the poverty and health relationship. A simple scatterplot of local foods on poverty (Figure 4) appears to support the correlation analysis provided in Table 2: there appears to be no relationship between the two. Projecting a simple non-linear regression line through the data suggests that there is a very weak concave relationship. The linear and squared parameters are statistically significant (i.e., zero is not contained in the confidence interval) the values of the parameters themselves are very small. A mapping of the interaction variable (Map 4) show a very strong spatial pattern that is similar to the local foods index (Map 2). This is particularly clear when comparing the Getis-Ord hot and cold spots.

Econometric Analysis The descriptive analysis above reveals that there are strong spatial patterns in the data with spatial clustering of not only our dependent variable ( ) but also many of our independent variables. To test if there is sufficient spatial dependence in the data we test for spatial autocorrelation in the ordinary least squares errors using the Moran’s I, the Lagrange Multiplier test and the Likelihood Ration test (Table 4). Given the descriptive analysis it not surprising that these three tests of the regression residuals confirms the presence of spatial autocorrelation. As such, classical regression analysis will provide biased, inconsistent and inefficient estimates (Anselin 1988; LeSage and Pace 2009).

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Table 4: Spatial Dependency in Regression Model Moran I Moran I 0.4450 Moran I-statistic 38.9056 Marginal Probability (0.0001) LM error test 1496.7739 (0.0001) 6.6350

LM value Marginal Probability chi(1) .01 value

LR test LR value

1233.7138

Marginal Probability

(0.0001)

chi-squared(1) value

6.6350

There are two approaches to addressing this spatial dependency within the data. The first is to “correct” for the dependency and the second is to acknowledge that there are spatial patterns in the data and to attempt to explore what those spatial patterns are. We explore both approaches by using a spatial lag estimator (SAR), spatial error estimator (SEM) and the spatial Durbin estimator (SDM):

SEM: SDM:

(

,

SAR:

β

,

)

u

, ,

(

), (

)

Here is a spatial weight matrix that is designed to identify adjacent counties, commonly referred to as a first-order rook’s contiguity matrix. If two (i,j) counties are adjacent, the individual elements of the spatial weight matrix ( ) takes on a value of one. If the two counties are not adjacent takes a value of zero. In practice this first-order rook’s contiguity matrix is row-standardized, or is rowstochastic. The spatial lag (SAR) and spatial error (SEM) models are designed to simply “correct” for the presence of spatial autocorrelation but does not directly capture how individual variables spillover or effect neighboring counties. The spatial Durbin model (SDM) is specifically structured to not only correct for spatial autocorrelation through but also . The set of parameters explicitly captures how, for example, local food related activities in county j influences health in county i.4

4

We can capture some of this spillover in the SAR model ( ) ( ) . Here we obtain the marginal effects:

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which can be solved for y, yielding

One potential problem with the three spatial models as specified is that the error structure, while exhibiting spatial dependency, is homoscedastic errors. Specifically the random component of the ( ), the variance is constant across models, for example with the spatial lag model (SAR) observations or space (i.e., ). Given the results of the Getis-Ord hot and cold spot analysis it is clear that there are strong spatial clusters such as concentrations of poverty and poor health in certain parts of the southern U.S. A more reasonable structure would be to allow the random component of the error structure to be heteroscedastic. Rather that assume the random errors to be homoscedastic, or ( ), we allow the error structure to be heteroscedastic, ( ) ( ). Here we allow the error variance to take on different values across space. We can express these heteroscedastic models as: BSAR: BSEM: BSDM:

(

, β

,

,

)

u

(

(

(

), (

)

)

) (

)

The set of variance scalars (v1, v2, . . . , vn) are unknown parameters that need to be estimated. While the homoscedastic spatial models can be readily estimated via maximum likelihood, the heteroscedastic spatial models can become untrackable in a maximum likelihood framework. As the set of variance scalars becomes large (i.e., n becomes large) the number of integrals becomes large making solving the likelihood untrackable. The alternative approach is to take advantage of prior information on the distribution of the relationships within a Bayesian framework (hence, BSAR, BSEM, BSDM for Bayesian Spatial Error Model and so on). We follow the approach of LeSage (1997, 2000) and LeSage and Pace (2009) and impose a prior distribution for the vi terms taking the form of an independent χ2(r)/r distribution where χ2 is a single parameter distribution with r as the parameter. By adding the single parameter r this allows the estimation of the n parameters vi. The prior distributions are indicated using (π ), a normal-gamma conjugate prior for σ and a uniform prior for ρ. To operationalize the Bayesian approach LeSage suggests a Markov Chain Monte Carlo (MCMC) estimation method which is based on the idea that a large sample from the posterior distribution of our parameters can be used in place of an analytical solution where this is difficult or impossible. The value of the joint posterior distribution of the parameters at any given point in the data constitute one iteration through the MCMC sampler. The estimates from this iteration are used as the initial values in the following pass. The Gibbs sampling procedure must be repeated until the values of the estimates converge. For this study we use 100,000 draws with the first 1,000 draws removed in effect acting as a ”burn-in” to minimize the likelihood of poor starting values. ( ) . This decomposes the total effect of X on y into two additive parts: the direct effect  and the indirect effect ( ) . The direct effect captures the marginal impact of a change in X on the dependent variable in the absence of spatial effects. When  ≠ 0, the indirect effect captures the impact of a marginal change in X on y due to neighborhood spatial effects.

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While the Spatial Durbin model (SDM), along with the reduced for spatial lag model (SAR) (see footnote 6), allow us to model the nature of the spillover relationships between the right-hand-side variables and health, it assumes that the parameter estimate ( ) is constant across space or is a global parameter. In effect, by construction, the relationship between poverty and health or local foods and health is the same across geography or space. It is not clear if this is a reasonable assumption. An alternative would be to lift this assumption and allow the parameters to vary across space then examine the patterns in those parameter estimates. Building on Casetti’s (1972) expansion method to explicitly model the properties of spatial nonstationarity in regression analysis and locally weighted regression (Cleveland 1979; Cleveland and Devlin 1988) Fotheringham, Brunsdon and Chartlon (2002) suggest a Geographically Weighted Regression (GRW) model. By allowing the underlying data generating process to vary over geographical space rather than assuming that the underlying process is constant over all locations. In essence, the GWR provides a systematic method for providing a unique parameter estimate for every observation in the sample. The GWR model can be written as:

GWR:

yi   0(ui, vi)   k  k (ui, vi) Xik   i,

where (ui,vi) indicates that location of the ith point and

(

)

 k (ui, vi) is a realization of the function

 k (u, v) at point i . The individual value of  k (ui, vi) is the value of the parameter for each observation. The GWR specification recognizes that spatial variations in the parameters might exist and provides the model with a way that they can be recognized. A crude way of thinking about this approach is to assign a slope dummy variable for every observation in the cross sectional data set then look for patterns in those slope dummies. One of advantages of using a range of different estimators to explore the poverty, local foods and health interface is to investigate the robustness of the results. If we find that the same basic relationships are under each of the different model specification or estimation methods, the robustness of our results and policy insights are affirmed. If we find that the results vary across models or estimators then we need to rethink our empirics such as how variables are measured, excluded variables (omitted variable bias) or unit of analysis before we can draw any conclusions about policy. Econometric Results There are three sets of results: the homoscedastic error models (Table 5), the heteroscedastic errors (Table 6) and the Geographically Weighted Regression model (Table 6). First consider the homoscedastic and heteroscedastic error models then the GWR results. Across all the spatial estimators we find that the spatial lag ( ) and spatial error ( ) to be statistically significant at the highest levels. This reaffirms the results of the Moran’s I, Legrange Multiplier and Likelihood Ratio tests reported in

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Table 4). Removing the ordinary least squares results from further consideration, the models explain between 73 to 82 percent of the variation in our health index. As expected, poverty has a strong positive association with our health index which means that higher poverty, after controlling for other factors including spatial dependency, is associated with poorer health conditions (recall higher values of our health index is associated with poorer health). Higher education is also associated with better health, as we expected. Access to health care, measured by general practitioner physicians, tends to be associated with better health, but the results are not robust across the different estimation methods. The social capital metric tends to be statistically insignificant in all the models save for the heteroscedastic errors Spatial Durbin Model (BSDM Table 6). The lack of consistent results on social capital does not allow us to draw any inferences. Now consider the spatial spillovers captured by the Spatial Durbin Models (SDM and BSDM). We find that there is a spillover effect with poverty and education as we would expect. Access to health care is negative and statistically significant, which is consistent with prior expectations. This latter result is particularly interesting because it tells us that having “direct” access (i.e., access within the county) is not necessarily important, but having “indirect” access (i.e., access in a neighboring county) does matter. The spatial spillover effect of social capital is not significant in the homoscedastic model but is significant heteroscedastic model. If one were to maintain that the heteroscedastic Spatial Durbin Model (BSDM) is the most flexible model we have somewhat unlikely results pertaining to social capital and health. Higher levels of social capital within a county seem to imply poorer public health, but higher levels in neighboring counties implies better health. This result is not intuitive and requires farther exploration. The central variables of interest are the local foods index and the interaction between local foods and poverty. Our local foods index is consistently associated with better health. But the spatial spillover effect, while negative and consistent with the central hypothesis, is statistically insignificant. The interaction term ( ) tends to be positive and statistically significant in four of the six (recall, the OLS model is removed from further consideration) models. This suggests that areas that are characterized as having higher concentrations of local foods related activities and higher poverty tend to have higher levels of poor health. Conversely, areas with lower concentrations of local foods and lower levels of poverty tend to have higher levels of good health. This seems to suggest that rather than access to local foods having a dampening effect on the poverty – health relationship, it seems to compound the problem. The spatial spillover captured by the Spatial Durbin Model is statistically insignificant. This analysis does not us to draw any inferences about those counties that are “off diagonal” or have high levels of one of these two attributes and low levels of the other (e.g., high local foods - low poverty). On the direct relationship between local foods and public health, one must take care to not make inferences about causation. Unlike Webber, Stephenson, Mayes and Stephenson (2013), we are unwilling to draw the conclusion that access to local foods related activity causes or results in better health. It is equally likely that people who are in better health demand local foods which reverse the direction of causation. One could think about this within the context of food deserts: is it a supply or demand problem? Is it that the availability of healthy foods or the lack of demand? Clearly, correlation, no matter how sophisticated the methodology, does not imply causation. 16 | P a g e

The results on the interaction term ( from Figure 1) are clearly not as expected. This result may speak to the quality of jobs that are associated with local foods. Many of these farms tend to be smaller with more limited revenues and hence wages/profits flowing to the farmer. This would be consistent with prior work that finds that higher concentrations of local foods is actually associated with lower growth rates in per capita income (Deller, Brown, Haines and Fortenbery 2013). These latter results has raised concerns over the economic viability of many farmers that enter the local foods market place. These results, however, are global estimates which assume that the relationship is constant across space. We test for to see if there are indeed spatial variations in the underlying data generating process by using Geographically Weighted Regression. The results of this analysis are presented in Table 7. A simple ANOVA analysis of the OLS and GWR generated error structure suggests that the GWR estimator is superior to OLS. On the one hand this is not surprising because of the spatial dependency in the data and the clear hot and cold spots identified by the Getis-Ord statistics. But this does suggest that the parameters of the model may vary over space. In Table 7 the individual coefficients that are estimated for each variable are ranked from lowest (minimum) to highest (maximum) and the lower and upper quartile values along with the median are reported. To test for the statistical significance Fotheringham, Brunsdon and Chartlon (2002) suggest a Monte Carlo simulation method first suggested by Hope (1968).5 Based on these Monte Carlo simulations we find that there are spatial variations in the relationship between poverty and health as well as social capital and health. There is no evidence of spatial variation with respect to local foods, education and access to health care. Nor is there any spatial variation in the relationship between the poverty and local foods cross product variable and health. Thus the global parameter estimates provided in Table 5 and 6 are reasonable for our local foods related results. While the lower and upper quartiles are informative (e.g., are they consistently positive, negative or do they switch direction?) a complete mapping of the results provides a more insightful way of looking at the results. Rather than mapping the individual coefficients as reported in Table 7 we report out the individual t-statistics, specifically if the coefficient is positive and significant, negative and significant or not significant. Consider Map 5 where we map the significance of the poverty on health index. For most of the U.S. the relationship is positive and significant, but there are parts of the western U.S. were the relationship is statistically insignificant. What this suggests is for the poverty – health relationship need not hold for the whole of the U.S. While the Monte Carlo simulations find no significance a mapping of the significance levels does provide some additional insights (Map 6). This analysis suggests that the local foods and health relationship does not hold for many parts of the U.S. The relationship holds for much of the region around the Mississippi River area of the country, parts of 5

In this process the observed value of the test statistic is compared with n – 1 simulated ones. The results are sorted, and the rank of the observed test statistic is determined. The p-value for the test is obtained by subtracting the ratio rank/n from unity. For this study the number of local model calibrations is set to 100. After the observed variance of the local parameter estimate is calculated and stored, 99 sets of variances are obtained for each variable based on different randomizations of the observed data. The p-value is then computed for the local parameters associated with each variable as described above. These p-values indicate whether the spatial variation is significant or it most likely occurred by chance.

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the Great Plains, some parts of the Rocky Mountains and the very southwestern part of the U.S. Unlike the poverty – health relationship which holds for most, but not all, of the U.S., the local foods – health relationship is much more place sensitive. Blanket generalizations that might be drawn from the analysis presented in the correlation and spatial econometric models (Tables 2, 5 and 6) along with the scatterplots may not apply to all parts of the U.S. For completeness we also map the statistical significance results for the local foods and poverty cross product variable (Map 7). For much of the U.S. the relationship of the cross product term and health is statistically insignificant, but there are pockets where the relationship is positive. This region roughly follows the same areas uncovered in Map 6. There are very small pockets of the U.S., such as part of the Ozarks, the Appalachian areas around West Virginia and parts of the New York, Vermont and Massachusetts border, where the relationship is negative. For these smaller regions the presence of higher local foods activity does indeed dampen the poverty and health relationship.

Discussion and Conclusions This study has attempted to better understand the poverty and health relationship through the lens of local foods. Does access to local foods related activities dampen the poverty and health relationship? Our work most closely follows that of Salois (2012) and while we find some evidence that reaffirms Salois, our results do not allow us to make firm policy recommendations. Like Salois, on one level of analysis we find that higher concentrations activities associated with local foods tends to be associated with higher levels of health. This would be consistent with the overriding theme of the food desert literature. What is not clear, however, is the direction of causation: is access to local foods driving health or are the health characteristics of people driving the demand for local foods? Second, if we allow the relationship to vary over space, or in our case U.S. counties, we find that the local food and health relationship does not hold for all U.S. counties. There are large parts of the U.S. where the relationship breaks down. We also find some evidence that the presence of local foods type activity does not mediate the poverty and health relationship but indeed exacerbates the relationship. This latter result challenges those that advocate that the introduction of local foods into food deserts will help improve health, at least from an ecological perspective. While there are certainly instances where local foods introduction into food deserts may help promote health, our results suggest that one cannot make blanket recommendations. An alternative explanation of this latter result might be that farms associated with local foods type production are not generating sufficient revenues to support reasonable wages, salaries or profits. While our results offer a cautionary tale, they are far from definitive. While we are comfortable with our measure of health, our measure of local foods could also be limited. We are missing much of the institutional sales market, such as farmers selling to restaurants, hospitals, local schools or other retailers. Unfortunately these later data is simply not available in any meaningful way. This is why our local foods index is designed to capture the characteristics of farm activity that tends to be associated with smaller scale production. In addition, our set of control variables is very limited (i.e., one measure 18 | P a g e

of education, social capital and access to health care). Our analysis may be subject to omitted variable bias. Also, our ecological approach of using county level data may be masking important variations within counties. Next steps must consider the sensitivity of our results to alternative measures of local foods as well as different measures of poverty. We must also explore alternative control variables to ensure that we are not assigning too much importance to the variables that are included due to problems with omitted variables. Finally, the local foods measures, particularly the farm related activity, are from the 2007 Census of Agriculture and may not adequately reflect the rapid growth in local foods over the past five years. When the 2012 Census of Agriculture becomes available additional work will be required.

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References Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers. Born, B. and M. Purcell. (2006). “Avoiding the Local Trap: Scale and Food Systems in Planning Research.” Journal of Planning Education and Research. 26:195-207. Brown, J.C. and M. Purcell. (2005). “There is Nothing Inherent About Scale: Political Ecology, the Local Trap, and the Politics of Development in the Brazilian Amazon.” Geoforum. 36:607-624. Cassetti, E. (1972). “Generating Models by the Expansion Method: Applications to Geographical Research.” Geographical Analysis. 4:81-92. Cattell, V. (2001). “Poor People, Poor Places, and Poor Health: The Mediating Role of Social Networks and Social Capital.” Social Science and Medicine. 52:1501-1516. Cleveland, W.S. (1979). “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association. 74: 829-836. Cleveland, W.S. and S.J. Devlin. (1988). “Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting.” Journal of the American Statistical Association. 83:596-610. Creamer, N.G. and R.D. Dunning. (2012). “Local Food Systems for a Healthy Population.” North Carolina Medical Journal. 73(4):310-314. Darmon, N. and A. Drewnowski (2008). “Does Social Class Predict Diet Quality?” The American Journal of Clinical Nutrition. 87(5): 1107-1117. Dasgupta, S., B. Laplante, H. Wang and D. Wheeler. (2002). “Confronting the Environmental Kuznets Curve.” Journal of Economic Perspectives. 16:147-168. Deller, S.C., L. Brown, A. Haines and R. Fortenbery. (2013). “Local Foods and Rural Economic Growth.” Paper prepared for presentation at the 2013 Annual Meetings of the North American Regional Science Association. Atlanta, GA (Nov). Dietz, T., E.A. Rosa and R. York. (2012). “Environmentally Efficient Well-Being: Is there a Kuznets Curve.” Applied Geography. 32:21-28. Dinda, S. (2004). “Environmental Kuznets Curve Hypothesis: A Survey.” Economical Economics. 49:431455. Fotheringham, S., C. Brunsdon and M. Charlton. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley. Getis, A. and J.K. Ord. (1992).”The Analysis of Spatial Association by Distance Statistics.” Geographical Analysis. 24:189–206.

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Goetz, S.J. and An. Rupasingha. (2006). “Wal-Mart and Social Capital.” American Journal of Agricultural Economics. 88(5):1304-1310. Harrington, M. (1962). The Other America: Poverty in the United States. New York: Macmillan. Hope, A.C.A. (1968). “A Simplified Monte Carlo Significance Test Procedure.” Journal of the Royal Statistical Society Series B 30:582-598. Keene, A. and S.C. Deller. (forthcoming). “Evidence of the Environmental Kuznets’ Curve Among US Counties and the Impact of Social Capital.” International Review of Regional Science. Kosa, J., A. Antonovsky and K. Zola. (eds). (1969). Poverty and Health: A Sociological Analysis. Cambridge, MA: Harvard University Press. LeSage, J.P. (1997). “Bayesian Estimation of Spatial Autoregressive Models.” International Regional Science Review. 20(1&2):113-129. 30 LeSage, J.P. (2000). “Bayesian Estimation of Limited Dependent variable Spatial Autoregressive Models.” Geographical Analysis. 32(1):19-35. LeSage, J.P. and R. K. Pace. (2009). Introduction to Spatial Econometrics. Boca Raton: Taylor Francis/CRC Press. Link, B.G. and J.C. Phelan. (1996) “Understanding Sociodemographic Differences in Health—The Role of Fundamental Social Causes.” American Journal of Public Health. 86: 471–473. Low, S.A., and S. Vogel. 2011. Direct and Intermediated Marketing of Local Foods in the United States. Research Report Number 128. USDA ERS: Washington DC. Mansfield, C. and L.F. Novick. (2012). “Poverty and Health: Focus on North Carolina.” North Carolina Medical Journal. 73(5):366-373. Montgomery, L.E., J.L. Kiely and G. Pappas. (1996). “The Effects of Poverty, Race, and Family Structure on US Children’s Health: Data from the NHIS, 1978 through 1980 and 1989 through 1991.” American Journal of Public Health. 86(10):1401-1405. Moreland, K.V., A.V. Diez-Rous and S. Wings. (2006). “Supermarkets, Other Food Stores and Obesity: The Atherosclerosis Risk in Communities Study.” American Journal of Preventive Medicines. 30:333-349. Murray, S. (2006). Poverty and Health. Canadian Medical Association Journal. 174(7):923-923. Olson, C.M. (1999). “Nutrition and Health Outcomes Associated with Food Insecurity and Hunger.” Journal of Nutrition. 129(2):521S-524S. Ord, J.K. and A. Getis. (1995). “Local Spatial Autocorrelation Statistics: Distributional Issues and an Application.” Geographical Analysis. 27:286–306.

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Pappas, G. S. Queen, W. Hadden and G. Fisher. (1993). “The Increasing Disparity in Mortality Between Socioeconomic Groups in the United States, 1960 and 1986.” The New England Journal of Medicine. 329(2):103-109. Pellertier, J.E., M.L. Laska, D. Neumark-Sztainer and M. Story (2013). “Positive Attitudes towards Organic, Local, and Sustainable Foods are Associated with Higher Dietary Quality among Young Adults.” Journal of the Academy of Nutrition and Dietetics. 113(1):127-132. Purcell, M. and J.C. Brown. (2005). “Against the Local Trap: Scale and the Study of Environment and Development.” Progress in Development Studies. 5(4):279-297. Rupasingha, A. and S.J. Goetz. (2007). “Social and Political Forces as Determinants of Poverty: A Spatial Analysis.” Journal of Socio-Economics. 36(:650-671. Rupasingha, A., S.J. Goetz, and D. Freshwater, (2006). ‘The Production of Social Capital in US Counties’, Journal of Socio-Economics. 35:83–101. Sadler, R.C., J.A. Gilliland and G. Arku. (2012). “Community Development and the Influence of New Food Retail Sources on thePrice and Availability of Nutritious Food. Journal of Urban Affairs. 35(4):471-491. Salois, M.J. (2012). “Obesity and Diabetes, the Built Environment, and the ‘Local” Food Economy in the United States, 2007.” Economics and Human Biology. 10:35-42. Szreter, S. and M. Woolcock. (2004). “Health by Association? Social Capital, Social Theory, and the Political Economy of Public Health.” International Journal of Epidemiology. 33:650-667. Wang, M.C., S. Kim, A.A. Gonzales, K.E. MacLeod and M.A. Winkleby. (2007). “Socioeconomic and FoodRelated Physical Characteristics of the Neighborhood Environment are Associated with Body Mass Index.” Journal of Epidemiology and Community Health. 61:491-498. Webber, K.H., T.J. Stephenson, L. Mayes and L. Stephenson. (2013). “Nutrition Knowledge and Dietary Habits of Farmers Markets Patrons.” World Applied Sciences Journal. 23(2):267-271. Zepeda, L. and J. Li. (2007). “Characteristics of Organic Food Shoppers.” Journal of Agricultural and Applied Economics. 39(1):17-28. Ziersch, A.M., F.E. Baum, C. MacDougall and C. Putland. (2005). “Neighbourhood Life and Social Capital: The Implications for Health.” Social Science and Medicine. 60(1):71-86.

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Table 1: Public Health and Local Food Indices Low Birth Weight Rate Adult Obesity Rate Adult Diabetes Rate. Premature Death (Years of Potential Life Lost) Poor or Fair Health (%)

Eigenvalues 0.3764 0.4383 0.4887 0.4746 0.4497 Variance Explained 0.6462

Number of Farms with Sales less than $100K per 1,000 Population Number of Organic Farms per 1,000 Population Number of Farms with Value Added Production per 1,000 Population Number of CSA Farms per 1,000 Population Number of Orchard, Fruit, Vegatable and Berry Farms per 1,000 Population Numner of Farms with Direct Sales per 1,000 Population Direct Sales per Capita Number of Farmers' Markets per 1,000 Population Variance Explained

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0.3995 0.3324 0.4309 0.3614 0.2597 0.4585 0.3173 0.1878 0.4624

Table 2: Simple Correlations PH

LF

PV

ED

DR

-0.5103 *** -0.2233 *** -0.3193 ***

0.5035 *** 0.2134 ***

0.1807 ***

-0.0138 -0.2456 *** -0.2379 *** 0.3771 ***

-0.5622 *** -0.2076 *** -0.3478 ***

0.5170 *** 0.2848 ***

0.2335 ***

-0.0099 -0.1609 *** -0.1639 *** 0.2592 ***

-0.3994 *** -0.1407 *** -0.2357 ***

0.3625 *** 0.1919 ***

0.1599 ***

Pearson Correlation Coefficients Local Foods Index (LF) Poverty Rate (PV) Education (Bachelor's Degree) (ED) General Practition Doctors per 1,000 Population (DR) Social Capital Index (SC)

-0.1479 *** 0.7247 *** -0.6590 *** -0.3482 *** -0.3451 ***

-0.0323 -0.1949 *** -0.1661 *** 0.3813 ***

Spearman Correlation Coefficients Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practition Doctors per 1,000 Population Social Capital Index

-0.1202 *** 0.7017 *** -0.6834 *** -0.3671 *** -0.3864 ***

-0.0818 *** 0.5185 *** -0.4940 *** -0.2537 *** -0.2604 ***

Kendall Tau b Correlation Coefficients Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practition Doctors per 1,000 Population Social Capital Index *** Significant at the 99.9 percent level. ** Significant at the 95.0 percent level. * Significant at the 90.0 percent level.

Table 3: Factor Analysis

Public Health Index Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practition Doctors per 1,000 Population Social Capital Index Cumulative Prior Communality Estimates: SMC

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Alpha

Harris Prin Comp

-0.8785 0.0766 -0.6713 0.8276 0.4483 0.4395

0.8580 -0.0430 0.7558 -0.7529 -0.4562 -0.3937

-0.8880 0.0566 -0.7997 0.8166 0.5797 0.5093

0.7427

0.9059

0.4489

SMC 0.6871 0.3228 0.5479 0.5782 0.2819 0.2795

Table 4: Spatial Dependency in Regression Model Moran I Moran I-statistic Marginal Probability

Moran I 0.4450 38.9056 (0.0001)

LM value Marginal Probability chi(1) .01 value

LM error test 1496.7739 (0.0001) 6.6350

LR value Marginal Probability chi-squared(1) value

LR test 1233.7138 (0.0001) 6.6350

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Table 5: Spatial Modeling Results (homoscedastic errors) Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practitioner Doctors per 1,000 Population Social Capital Index (Local Foods Index)*(Poverty Rate) W*(Local Foods Index)

OLS -0.2803 *** (0.0001) 0.1332 *** (0.0001) -0.1532 *** (0.0001) -0.3953 *** (0.0001) 0.0041 (0.8393) 0.0040 ** (0.0345) ―

SAR -0.1577 *** (0.0001) 0.0843 *** (0.0001) -0.1014 *** (0.0001) -0.1743 ** (0.0027) 0.0129 (0.4078) 0.0037 ** (0.0120) ―

SEM -0.1564 *** (0.0001) 0.1028 *** (0.0001) -0.1323 *** (0.0001) -0.0535 (0.3671) 0.0021 (0.9140) 0.0041 ** (0.0093) ―

W*(Poverty Rate)

―

―

―

W*(Education (Bachelor's Degree))

―

―

―

W*(General Practitioner Doctors per 1,000 Population)

―

―

―

W*(Social Capital Index)

―

―

―

W*[(Local Foods Index)*(Poverty Rate)]

―

―

―

ρ

―

λ

―

0.6864 Marginal significance in parentheses. *** Significant at the 99.9 percent level. ** Significant at the 95.0 percent level. * Significant at the 90.0 percent level.

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0.5300 *** (0.0001) ―

0.7272

― 0.6540 *** (0.0001) 0.8234

SDM -0.1397 *** (0.0001) 0.0876 *** (0.0001) -0.1308 *** (0.0001) -0.0381 (0.5046) 0.0177 (0.3569) 0.0042 ** (0.0050) -0.0549 (0.2439) -0.0328 *** (0.0001) 0.0776 *** (0.0001) -0.3433 ** (0.0030) 0.0207 (0.4397) -0.0001 (0.9710) 0.6660 *** (0.0001) ―

0.7482

Table 6: Spatial Modeling Results (heteroscedastic errors) Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practitioner Doctors per 1,000 Population Social Capital Index (Local Foods Index)*(Poverty Rate) W*(Local Foods Index)

BSAR -0.1092 *** (0.0001) 0.0814 *** (0.0001) -0.0975 *** (0.0001) -0.1614 ** (0.0027) 0.0079 (0.3144) 0.0004 (0.3918) ―

BSEM -0.1643 *** (0.0001) 0.1057 *** (0.0001) -0.1329 *** (0.0001) -0.0886 (0.1037) 0.0013 (0.4869) 0.0040 ** (0.0256) ―

W*(Poverty Rate)

―

―

W*(Education (Bachelor's Degree))

―

―

W*(General Practitioner Doctors per 1,000 Population)

―

―

W*(Social Capital Index)

―

―

W*[(Local Foods Index)*(Poverty Rate)]

―

―

ρ λ

0.5381 *** (0.0001) ―

0.7285 Marginal significance in parentheses. *** Significant at the 99.9 percent level. ** Significant at the 95.0 percent level. * Significant at the 90.0 percent level.

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―

BSDM -0.1114 *** (0.0001) 0.0858 *** (0.0001) -0.1296 *** (0.0001) -0.0277 (0.3113) 0.0675 ** (0.0017) 0.0018 (0.1279) -0.0433 (0.1716) -0.0347 *** (0.0001) 0.0772 *** (0.0001) -0.2688 ** (0.0055) -0.0520 ** (0.0313) 0.0002 (0.4777) 0.6749 *** (0.0001)

0.6696 *** (0.0001) 0.8251

0.7469

28 | P a g e -3.6939 -1.2127 -0.0505 -0.3376 -1.3515 -0.5135 -0.0259 0.6868 0.8470 13.7677

OLS Adjusted R2 GWR Adjusted R2 ANOVA GWR vs OLS F Staistic

Minimum

Intercept Local Foods Index Poverty Rate Education (Bachelor's Degree) General Practitioner Doctors per 1,000 Population Social Capital Index (Local Foods Index)*(Poverty Rate)

Table 7: Geographically Weighted Regression Analysis

-0.2119 -0.2722 0.0760 -0.1485 -0.3279 -0.1452 -0.0045

Lower Quartile 0.0660 -0.1660 0.1014 -0.1303 -0.1199 -0.0183 0.0040

Median 0.5634 -0.0543 0.1213 -0.1146 0.2300 0.1420 0.0121

Upper Quartile 3.1188 0.3512 0.2410 -0.0355 2.9582 0.9584 0.0529

Maximum

(0.001) (0.390) (0.001) (0.340) (0.230) (0.001) (0.470)

Monte Carlo Simulation (p-value)

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Map 1

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Map 2

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Map 3

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Map 4

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Map 5

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Map 6

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Map 7

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