Finding the Hidden Wells within Food Deserts:

Accessing and Measuring Racial Disparities according to Food Store Types in the Milwaukee Metro Area

By Mark Caldwell

Overview

•Theoretical Scope•Importance of Study•Data and Methods •Preliminary Analysis•Negative Binomial Regression: White,

Black and Latino•Results and Predicted Count Probabilities•Limitations •Areas of Future Inquiry

Theoretical Scope• Theories which associate Race/SES and Food Store Types:• Community Food Security envisions food systems that are

decentralized, supportive of equitable food access and created by consumer based decision-making (Anderson and Cook, 1998)

• Accessibility to food stores and supermarkets decreases rates of diabetes and obesity. “Neighborhood effects” of this kind have long-term consequences for the health of that community (Cummins and Macintyre, 2005)

• A multivariate analysis conducted on the availability of food store outlets in the US in association with neighborhood characteristics on race, ethnicity and SES status. Lower income and minority neighborhoods have less availability to supermarkets (Powell et. al, 2007) (Larson et. al, 2009)

• While these studies address the physical reality of where food stores are distributed, there are two places where this study fills the gap:

• 1. Including a “neighborhood effects” theory regarding food stores as they relate to Race controlling for SES

• 2.Defining a new provisional food type that includes non-profit food organizations and for profit businesses

Spatial Distribution of Healthy Food Locations

Spatial Distribution of Unhealthy Food Locations

Spatial Distribution of Provisional Food Locations

Importance of Study

• “Food deserts” are considered those places in the city where access/availability of nutritional food is non-existent

• Non-profit organizations seeks to fill the void in these food deserts through community outreach programs

• Focal Questions: How does race associate with the locations of these provisional food locations?

• And additionally: How do healthy and unhealthy food store types associate with race?

Focal Relationship: Race/Class and Three Food Store Types

Healthy Food Stores

Unhealthy Food Stores

Provisional Food Stores

Race: White Population Percentage

Main Dependent VariablesMain Independent Variable

Total Population

Education: Bachelors

Degree of Better

Total Square Miles of Census

Tract

Median Income ($)

Control Variables

Definitions of Food Store Types

• Healthy: Grocery Stores and Supermarkets. These are locations where healthy food options are available to purchase

• Unhealthy: Convenience Stores, Fast Food, and Food Markets. Locations that lack healthy food options such as fresh produce and meats.

• Provisional: are constituted by emergency food locations, food buying club drop-off sites, farmer’s markets and community supported agriculture (CSA) drop sites.*

• *These sites are being used together because they fall within the realm of businesses and non-profit organizations that are decided by the consumer base which they serve with a common mission goal to provide food accessibility.

Data and Methods

Unit of Analysis:• 304 Census tracts delineated by the Milwaukee City Limits

Data:• I utilized my own compilation of data that has two components: geocoded food

store locations and census tract information regarding socioeconomic indicators for Milwaukee city limits. First the three types of food locations: unhealthy, healthy, and provisional where acquired using two data streams. The first stream utilized the Reference USA: US Businesses database to obtain these food destinations. The second stream consisted of my personal social network with non-profit organizations, farmers markets and actors within the food justice movement who provided the locations of my “provisional locations.” These locations were obtained through data sent me for research purposes or were readily available of company websites or online community forums.

• Census tract information: Median Income, Total Population, Total Square Miles, Racial Composition and Educational Outcomes

Methods:After running a countfit command, a Negative Binomial model was run for

all three food store varieties.

Preliminary AnalysisTable 1: Mean Scores, Std. Error and Confidence Interval for All Independent Variables

  Mean Std. Err. [95% Conf. Interval] # of ObsIndependent Variables: White Pop Percent 64.09696 2.092446 59.97939 68.21453 304Black Pop Percent 30.52721 2.075815 26.44237 34.61205 304Latino Pop Percent 11.81064 1.058523 9.727655 13.89363 304Median Income($) 37906.68 1012.634 35913.99 39899.36 304Bachelors Degree or Better 0.211151 0.0104034 0.1906791 0.2316234 304Total Square Miles 0.820066 0.0788319 0.6649384 0.9751932 304Total Population 2974.98 82.21232 2813.201 3136.76 304

Correlation Matrix for Food Store Types Unhealthy Healthy ProvisionalUnhealthy 1 1.495 0.1463Healthy 0.1495 1 0.1588Provisional 0.1588 0.1463 1

Univariate and Bivariate Distributions of Independent Variables show no issues with Multicollinearity, non-normal distribution and bivariate outliers.*

• Census Tracts 3, 36, and 87 were shown to have “0” in there Median Income slot,

this was checked with property listing and land use maps of Milwaukee to concludethat these three tracts are zoned industrial and thus have no inhabiting populations which make an annual income

NBREG: White, Black and Latino

White Population Percentage

Black and Latino Population Percentage

VARIABLES Healthy Food Type Unhealthy Food Type

Provision Food Type

Black Pop % 0.0109*** -0.00489** 0.00180

(0.00259) (0.00236) (0.00330)

Latino Pop % 0.0167*** -0.00212 0.00536

(0.00360) (0.00413) (0.00531)

Median Income -1.10e-05 -1.67e-05** -1.72e-05**

(7.29e-06) (5.39e-06) (7.54e-06)

BA or higher -0.333 1.194** 1.881**

(0.584) (0.481) (0.621)

Square Miles -0.223** 0.0672 0.0150

(0.0986) (0.0710) (0.0762)

Total Population 0.000192*** 7.71e-05 0.000107

(4.98e-05) (5.25e-05) (6.44e-05)

Constant -0.447 1.240*** -0.707

(0.340) (0.280) (0.387)

Observations 304 304 304

VARIABLES Healthy Food Type

Unhealthy Food Type

Provision Food Type

White Pop % -0.00308 0.00368 0.000423(0.00193) (0.00201) (0.00276)

Bachelors Degree

-1.539** 1.339** 1.569**

(0.557) (0.439) (0.565)

Total Population

0.000271*** 7.48e-05 0.000119

(5.20e-05) (5.16e-05) (6.46e-05)

Median Income -1.84e-05** -1.63e-05** -1.80e-05**

(7.37e-06) (5.32e-06) (7.52e-06)

Square Miles -0.356** 0.0740 -0.00425(0.109) (0.0698) (0.0767)

Constant 0.685*** 0.788*** -0.541**(0.204) (0.201) (0.258)

Observations 305 305 305

Standard errors in parentheses *** p<0.001, ** p<0.05, * p<0.01

Results: Income, Education and Population

Income:1. Healthy= 100*exp^(-1.84e-05-1)= -36.78%2. Unhealthy = 100*exp^(-1.63e-05-1)=-36.78%3.Provision = 100*exp^(-1.80e-05-1)= -36.78%Population: Not statistically significant for

unhealthy and provisional sites1. Healthy= 100*exp^(0.000271-1)=36.75%Education: 1.Healthy = 100*e^(-1.539-1)=-21.45%2.Unhealthy= 100*e^(1.339-1)=381.52%3. Provision = 100*e^(1.569-1)=480.18%

Results: Interpreting Racial Percentage

• White Population Percentage:1. Healthy = 100*e^(-.00308-1)=-36.67%“The model predicts that the count number of healthy food

types will be approximately 37% lower for every percent increase in the White population within census tracts.”

2. Unhealthy = 100*e^(.00368-1)=36.92%“The model predicts that the count number of unhealthy

food types will increase by a factor of 1/3 (37%) for every percent increase in the White population within census tracts.”

3. Provision= 100*e^(0.000423-1)=36.80%“The model predicts that the count number of provisional

food types will increase by a factor of 1/3 (37%) for every percent increase in the White population within census tracts.”

Limitations

• The model predictions run counter to my original hypothesis, showing that white population percentage is positively associated with the number of unhealthy and provisional types, while negatively associated with healthy food locations.

• A potential explanation may lie in the notion that healthy food companies attempt to locate there operations in areas where there isn’t market saturation but still have high income and educational achievement demographics

• The provisional food types is limited based on the researchers data set. Since this analysis has begun, an additional 55 new locations have been identified which fit into this food type category. This would change the results significantly.

• The definition of “healthy” and “unhealthy” are broad concepts that allows for supermarkets to sell unhealthy products and fast food restaurants to offer “healthy meal options”

Areas of Future Inquiry

• I would like to create some predicted probability graphs to show how the food counts are affected by substantial racial percent changes (20% intervals)

• I thought about conducting a location quotient analysis utilizing community centers and churches as points which food locations could then be ascribed a set buffer ring. These new polygon units could then be overlaid with Census tract information

• I need to expand my literature review and theory sections to try to account for the results. Additional theories might include Jargowsky’s “social isolation,” and Massey and Denton’s “racial stratification,” and Brooks-Gunn “neighborhood poverty.”

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