(Author’s note: This is a reprint of the summary report I turned in to my faculty adviser for the fall quarter. As this was my last quarter at Cascadia State, this project is, in one sense, complete. However, we still need to eat; we still want to localize our economic activity; and we’re still a low-income family. I also want to keep writing. As you could probably tell from my recent series on Thanksgiving and Black Friday, my interest in economic justice and sustainable living extends beyond the food we eat and the clothes we wear to the ethics and values that drive our decisions, and the myriad connections that tug between society and the natural environment. I think this is a suitable platform for exploring these ideas, and as time permits I will do so.)
Final Project Report
Local Foods and Low-Income Families
December 21, 2011
Over the past year, I have been asking what happens when a low-income family tries to reduce its food-mile footprint and increase its food security. Much of this inquiry has been place-bound, as I have focused on the region where I live and the resources available to us. Yet the issues we face are neither unique nor confined to our own backyard. A persistent recession; the erosion of the middle class; greater demand for social services; the growing hegemony of multinational corporations; increasing urbanization of the population; the multiple-front threat of global climate change; all intersect and affect our food security. Low-income people are disproportionately vulnerable to such effects, even in the United States. Indeed, our social infrastructure is such that “it takes money to make money;” that is, access to capital often depends on having capital in the first place.
I wondered if we were living “in a bubble,” with an unusually high level of access to healthy, local food. Was there something unique or special about our community? To try and answer this question, I selected a number of cities and towns we had visited on a cross-country road trip the previous summer and conducted a survey of their basic demographics and local-food resources. I wanted to combine my first-hand observations with supporting data to get a more complete picture of these communities and be able to compare them one with another. In keeping with Einstein’s dictum that experiments should be as simple as possible, but no more so, I tried to be selective with the demographic data. I suspected that population and median income were going to be the main parameters, so I built my data set around them. I relied on population data from the U.S. Census Bureau for accuracy and consistency. I wound up using Census data for income as well. The Census Bureau hosts a variety of tools for gathering and analyzing income data, varying by methodology, emphasis, and results. Based on my criteria, I chose to use the SAIPE, or Small Area Income and Poverty Estimates program, which develops poverty estimates by combining survey results, administrative records, and statistical modeling. This was recommended for income and poverty estimates for smaller population areas. The average poverty rate for the cities and towns we visited was 14.7%, just below the current national average of 15.1%. This fact gives me confidence that we went through a fairly representative portion of the United States.
Developing my other criteria was a little more subjective. “Accessibility” can be defined in many ways; enhanced or constrained by many factors; and I had to fight my own tendency to over-think things. What drove my thinking here was, first of all, my experience over the last year in trying to obtain our food both affordably and locally, and the obstacles and opportunities I had found. Secondly, the observations I made and the impressions I formed while traveling across the country this summer, being the inspiration for this project, had an informing effect as well. Some discussion of these criteria is in order here.
“Food deserts” are a much-studied topic in food policy circles these days, as researchers and policymakers discover the unequal nature of food access across the nation. Geography plays a role in access, but so do economics, transportation networks, work schedules, and other social factors. I used a “Food desert Locator” developed by the Economic Research Service of the United States Department of Agriculture to determine whether a city or town in question was in or near a food desert, reasoning that proximity to one would by definition lead to diminished access to fresh food. The further away from a food desert a town was, the higher score it got.
Farmer’s markets are arguably the most direct means for consumers and producers to interact on a sizeable scale, yet they count few customers among low-income people. Briggs et al. (2010) report that as recently as 2009, a mere 0.008% of Supplemental Nutrition Assistance Program transactions occurred at farmer’s markets. Nonetheless, a farmer’s market is an indicator of an active local-foods network and a potential nexus of people and ideas, where capital can be exchanged and community ties strengthened. I tracked both the number of markets near the communities in question and whether or not they accepted SNAP benefits; the vast majority did not. Locating the markets was somewhat of a challenge too. The Agricultural Marketing Service of the USDA hosts a zip code-based locator, but it suffers from incomplete data. Considering that the number of markets in the United States has climbed 17 percent in 2011 to 7,175, in may come as no surprise that the USDA has trouble keeping track of them. I was able to verify some markets with the search tool at Local Harvest, though some of their listings were admittedly out-of-date.
Interestingly, there is a farmer’s market locator “app” for the iPhone. I could probably go on at length about this as it pertains to the topic at hand, but briefly the point is that such technology, while probably beneficial in a global sense, does little to democratize farmer’s markets to the point where the poor can access them. I fear that such a technological fix is on the other side of a digital divide which separates the poor from the rest of society, and strikes me as the opposite of what Feagan (2007) termed “democratic and equitable social relations” which he feels are necessary for a transformation of the political dialogue of place.
Community-supported agriculture programs, or CSAs, are another means of connecting small producers to local consumers, but I chose not to track them. First of all, the questions of localism and accessibility were rather difficult to answer. Is ten miles too far for a poor person to drive for their pickup? How about 20 or 50? Many CSAs deliver, but some travel 100 miles or more to do so. How does that affect cost and ecological footprint? Ultimately, though, it was more about my experience that CSAs are not an effective means for low-income people to access fresh, local food due to their cash-on-the-barrelhead model.
The community food co-op is another indicator to me of a healthy awareness of local food issues and a strong local-foods community. I looked for co-ops but only found them in 25% of surveyed cities and towns. I had to distinguish between “virtual” co-ops, which I defined as a producer-consumer owned distribution network, similar to a CSA; and “actual” co-ops, brick-and-mortar stores with inventory, staff, and all the associated activity that comes from such an enterprise. I probably found as many of the former as the latter, and I can see the benefits of the system, but I wanted to count on-the-ground entities where customers could network, learn about new foods and preparation techniques, and access capital which could improve their own food situation.
I am especially fond of community gardens as agents for empowering citizens to take ownership both of their neighborhood and their food supply. I have a hypothesis that a community garden is an effective means of distributing the sort of capital necessary to develop greater food independence as well as a mark of an engaged community. I found that half of the surveyed communities had at least one community garden, which I see as a hopeful trend.
Gleaning is another means of taking advantage of marginal resources, whether from a backyard garden or a grocery store; some groups manage to move millions of pounds of produce per year from the waste stream to the table. Surprisingly, I found more gleaning groups (9) than brick-and-mortar co-ops (6).
I’m also an advocate of patronizing locally-owned or regionally-based grocery stores whenever possible, and so was pleased to find at least one almost everywhere we went. This was another challenging category to quantify: how does a grocery store differ from a supermarket? How big can a company get and still be considered local? I may not have been entirely consistent here; I went as much by hunch as numbers. If a chain looked too big or was owned by another corporation, I passed.
My methodology for determining relationships between demographics and local-food access was fairly straightforward. After gathering the demographic and access survey data, I added all the “access points” for each city or town to derive an access score. This score is not weighted. Next, I calculated the correlation coefficient between the access scores and each of the four major demographic categories I had: population; median income; percentage of population in poverty; and number of people in poverty, extrapolated from population and poverty rate. I ran those numbers for the data set as a whole, then broke the group of cities down by population and looked within the smaller sub-groups, running the same calculations. The overall results were clear: population is the biggest factor correlating to access, with a correlation coefficient of 0.83.
The average access score went from 9 in the sub-10,000-population subgroup, to 13.4 in the 10-30,000, to 24 in the 31-55,000, topping out at 41 in the over-100,000 population subgroup. Clearly there are tipping points along the population curve where services grow dramatically, but it would take a more detailed analysis to discover them. There must be many confounding factors among a collection of cities and towns such as this, and further exploration and analysis would surely tease them out. Education level; percentage of farm employment; acres nearby under agriculture; presence nearby of a university; are all factors that could potentially make a difference in the level of food awareness and resource access.
The other results were equally interesting, if not as clear. In cities of under 10,000 and over 100,000, income was the biggest correlating factor at 0.82 and 0.93, respectively. In the other categories, however, nothing stood out as clearly:Meanwhile, how does my own community compare to the others? While it has the highest population of cities in its category of 31-55,000 population, it also has the lowest median income at $40,097 and the highest poverty rate, at 21%. Yet its access score of 29 is second-highest in its category and well above the overall average of 17. What can explain this? For one thing, there are two state universities, both land-grant colleges, with a long tradition of agricultural research. Add in a population with above-average levels of education and a knack for networking and the effects can be significant. Looking at the list of other cities surveyed in the same population bracket, I suspect that we here enjoy a rare level of advantage.
Numbers don’t tell the whole story, though. Take Laurel, Nebraska, for instance. This pleasant town of 964 people scored dead last with an access number of 1, yet we stocked our ice chest and dry food box nicely at the IGA in town and were quite charmed by its historic buildings and friendly people. Binghamton, New York scored a 16, just one below average, but we had a difficult time in the grocery store, found the locals to be odd ducks indeed, and couldn’t wait to leave.
While this nation is a broad and diverse collection of communities of all sizes, shapes, and forms, my analysis clearly reveals the role that population and income play in fostering the development of food-access resources, especially for low-income people. More research will be needed to help determine what policies, from both the local and the federal levels, can enhance the development of these resources.
“2010 Census Interactive Population Search.” 2010 Census. United States Census Bureau. web. 20 Dec. 2011. <http://2010.census.gov/2010census/popmap/ipmtext.php?fl=30>.
The Census Bureau of the U.S. Department of Commerce tallies population in the United States every 10 years, as mandated in the constitution. This site breaks that population down to the county and city level in an easy-to-use format.
Briggs, Suzanne, Andy Fisher, Megan Lott, Stacy Miller, and Nell Tessman. Real Food, Real Choice: Connecting SNAP Recipients with Farmers Markets. Portland, OR: Community Food Security Coalition, 2010. Print.
This is an in-depth study of the barriers that exist between farmer’s markets and low-income shoppers. It is a good analysis of the problem and makes some worthwhile recommendations, while asking some key questions.
Feagan, R. “The Place of Food: Mapping out the ‘local’ in Local Food Systems.” Progress in Human Geography 31.1 (2007): 23-42. Print.
Feagan’s 2007 review of the state of localism has been foundational to my view of this topic, and I have mined this article’s references for further reading.
“Food Desert Locator.” USDA Economic Research Service – Home Page. United States Department of Agriculture. Web. 21 Dec. 2011. <http://www.ers.usda.gov/data/fooddesert/>.
This is a great application of GIS technology. Searchable by address or zip code, cities are flagged with pop-up “data cards” for additional reference. Plus, the maps are big enough to be useful.
Local Harvest / Farmers Markets / Family Farms / CSA / Organic Food. Web. 20 Dec. 2011. <http://www.localharvest.org/>.
Local Harvest has a fairly complete and up-to-date database of farmer’s markets, CSAs, co-ops, and the like. Too bad their maps are so small.
“Model-based Small Area Income & Poverty Estimates (SAIPE) for School Districts, Counties, and States – U.S. Census Bureau.” Census Bureau Home Page. United States Census Bureau, Nov. 2011. Web. 20 Dec. 2011. <http://www.census.gov/did/www/saipe/>.
The SAIPE is the recommended income analysis tool for studying current conditions in smaller population areas due to its statistical-modeling methodology. The database design made it easy to drill down to the county level for information.
United States Department of Agriculture. United States Department of Agriculture. Web. 20 Dec. 2011. <http://www.ams.usda.gov/AMSv1.0/farmersmarkets>.
The Agricultural Marketing Service of the USDA hosts a farmer’s market locator which is quite easy to use, if somewhat incomplete.