Food Security in Newark: Methodology

In the developed world, food insecurity is a variable that can be difficult to measure with population-level data. For this reason, surveys are an appropriate measure of food insecurity and have been used extensively in past research on food insecurity in urban enclaves of the United States (Curtis & McClellan, 1995; Dinour, Bergen, & Yeh, 2007; Himmelgreen et al., 2000; Schneider, Rodgers, & Cheang, 2008). As the focus of this study is on Newark, New Jersey, a poll is an appropriate means of collecting information about the phenomenon of food insecurity as it manifests locally. 

Data Collection from People

According to the United States Census Bureau, the 2013 population of Newark was 278,427 (Census, 2013). There is extensive evidence in Census data that Newark is impoverished in relation to the rest of New Jersey, which in turn supports the thesis that food security is likely to exist in Newark. In particular, the Census data included the following observations:

• Per capita income in Newark is $16,972, as compared to $36,027 for the state of New Jersey as a whole

• Newark’s percentage of people below the poverty level is 29.1%, as opposed to 10.5% for the state of New Jersey as a whole

• Median household income in Newark is $33,960, as opposed to $71,629 for the state of New Jersey as a whole   

In order to understand food insecurity in Newark, an attempt ought to be made to survey residents of Newark in a systematic manner. For example, one polling strategy is to engage in the practice known as stratification, in which the members of a sample are chosen to ensure equal representation of variables in which the researcher is interested (Balnaves & Caputi, 2001; Creswell, 2009; Creswell & Plano Clark, 2011; Hesse-Biber, 2012; McNabb, 2010; Moustakas, 1994; Yin, 2009). In this study, for example, obtaining an accurate measure of the incidence of food insecurity in Newark requires sampling people from a variety of neighborhoods, including poorer and wealthier neighborhoods. 

Polling of Community Organizations

In this study, the polling of community organizations is closely connected to the polling of individuals. In attempting to gather data from people who might be experiencing food insecurity, one important problem is access, not mention food waste in other regions. People who are experiencing food insecurity might be difficult to reach, because they might also be homeless, in unpredictable living conditions, institutionalized or semi-institutionalized, or otherwise vulnerable. Community organizations, which have close and often long-established relationships with individuals who are experiencing food insecurity, are therefore an obvious research partner in the goal of gathering data from individuals with food insecurity, and they also can serve as important data contributors in their own right.

In this study, the polling of community organizations will begin with a general search for all organizations, particularly non-profit organizations, that serve impoverished residents of Newark in any capacity. These organizations will then be contacted by both telephone and email in order to serve as conduits to people with food insecurity. Community organizations can serve as places where the research questionnaire can be distributed, or where people experiencing food insecurity can be asked to fill out forms on provided computers. 

In addition to serving as points of connection to individuals experiencing food insecurity, community organizations will be asked to contribute data to triangulate the findings that are obtained by surveying individuals. Community organizations will be asked to provide the following information:

• How many people experiencing food security have they served in the past year?  

• What kinds of interventions have been provided for people with food insecurity?

• Where are the geographic clusters in which people with food insecurity have been observed to live?

• What are the gender and racial patterns in the distribution of food insecurity?

Some of these findings, such as the geographic cluster findings, will complement the overall research methodology, whereas other findings will be used to complement the data analysis supported by the individual polling. 

Polling of Government Agencies

The Essex County Division of Welfare, based in the heart of Newark, will be polled for this study. The Essex County Division of Welfare will be asked to provide the following data:

• The number of Newark residents who are currently on food assistance, and their geographic locations

• The number of Newark residents who have historically been on food assistance, and their geographic locations

• The number of Newark residents who have sought medical help for starvation

• Why certain geographies within the city might be particularly vulnerable in terms of food security

• What kinds of leadership actions are being taken to assist people with food insecurity in Newark?

These data will be used to triangulate findings from community organizations and individuals.

Figure 1 contains a geographic information system (GIS) representation of Newark sorted by two main factors, the prevalence of public housing developments (indicated by blue dots) and multi-family assisted housing (indicated by red dots) (CommunityCommons, 2015). This type of GIS overview is a means of understanding the distribution of poverty through Newark, which in turn can assist in a stratification plan for a survey of food insecurity.   

Figure 1 omitted for preview. Available via download).

In the absence of population-level data on food insecurity in Newark, the following methodology approach needs to be taken. First, a GIS tool such as that of Community Commons (2015) can be utilized to pinpoint the poverty level of specific neighborhoods in Newark. Once this analysis is carried out, the stratified sampling plan can be designed to include a sufficient number of participants from each neighborhood. Participants will be asked to fill out Dinour et al.’s (2007) Food Insecurity Survey utilizing either the online Survey Monkey platform or a paper version of the survey. 

Food security is a problem that is related not merely to income but to many other predisposing and explanatory factors. According, in addition to being asked to complete the Dinour et al. (2007) Food Insecurity Survey, participants will be asked to provide information on the following:

• Income

• Gender 

• Race

• Highest level of education completed (grade number, with 16 representing college completion and 20 representing graduate school completion)

• Age

• The language spoken at home

• Neighborhood (categorical variable for use in GIS representation, not regression)

Hence, the regression model for the study will be as follows, with the error term omitted: 

Food insecurity = b0 + b1Income + b2Gender + b3Race + b4Education + b5Age + b6Language  

In the statistical model of the study, the degree of food insecurity, as measured by Dinour et al.’s (2007) instrument, will serve as the dependent variable, with the independent variables being the 6 variables listed above. Age, income, and education level will be continuous variables. Gender will be a dummy variable (0 = man, 1 = woman), race will be a dummy variable (0 = not black, 1 = black), and language spoken at home will be a dummy variable (0 = English, 1 = language other than English). In this way, the regression will measure the extent to which being female, being black, and not speaking at English add to food insecurity in Newark.

Community organizations will be polled in order to gather data about the qualitative nature of food insecurity, including explanations for food insecurity. These data will be useful when forming recommendations about alleviating food insecurity in the city. Government agencies will be polled in order to establish a baseline understanding of the existence prevalence estimate for food insecurity against which the findings yielded by the study will be compared. In particular, the Newark Department of Health and Community Wellness will be polled in order to identify the number of Newark residents on food stamps, summer food service programs, and hospitalized for malnutrition.    

In this study, GIS has two uses. First, GIS will be utilized to identify neighborhoods from which participants need to be sampled in order to ensure that the sample contains a roughly equal weighting of poorer and wealthier residents and that geographic factors possibly related to food security are also represented in the sample. Second, GIS will be utilized to map the findings of the study. Once food insecurity is measured, then two simple procedures can allow the creation of a shapefile that can illustrate food insecurity by neighborhood. First, because each participant in the study hails from a neighborhood, the total level of food insecurity can be calculated at a neighborhood level. Second, once food insecurity is calculated at the neighborhood level, this information will be associated with a shapefile of Newark’s neighborhoods that will be requested from the city or downloaded from a public or private data source. Stata’s spmap and other GIS-related functions will be used to enter the food insecurity data and to generate a neighborhood-based food insecurity map of Newark.     

The use of GIS and the statistical model of the study will complement each other. The regression model of the study will allow the level of food security of an individual in Newark to be predicted on the basis of the individual’s income, gender, race, language spoken at home, education, and age. The GIS map will make it possible to determine if there are geographic patterns in the distribution of food insecurity. For example, overlaying the shapefile of food insecurity over an existing map of assisted housing, such as that provided in Figure 1, can disclose whether the distribution of food insecurity overlaps with the provision of public housing. These are other insights will be easier to generate if the regression model is combined with the use of GIS as described in this discussion of the study’s methodology.

References

Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach. Thousand Oaks, CA: Sage.

Census. (2013). Newark quick facts. from http://quickfacts.census.gov/qfd/states/34/3451000.html

CommunityCommons. (2015). Maps. from http://maps.communitycommons.org/viewer/?action=open_map&id=1342

Creswell, J. W. (2009). Research methods. Thousand Oaks, CA: Sage.

Creswell, J. W., & Plano Clark, V. (2011). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.

Curtis, K. A., & McClellan, S. (1995). Falling through the safety net: poverty, food assistance and shopping constraints in an American city. Urban Anthropology and Studies of Cultural Systems and World Economic Development, 24(1/2), 93-135. 

Dinour, L. M., Bergen, D., & Yeh, M.-C. (2007). The food insecurity–obesity paradox: a review of the literature and the role food stamps may play. Journal of the American Dietetic Association, 107(11), 1952-1961. 

Hesse-Biber, S. N. (2012). Mixed methods research: Merging theory with practice. New York, NY: Guilford Press.

Himmelgreen, D. A., Pérez-Escamilla, R., Segura-Millan, S., Peng, Y.-K., Gonzalez, A., Singer, M., & Ferris, A. (2000). Food insecurity among low-income Hispanics in Hartford, Connecticut: implications for public health policy. Human Organization, 59(3), 334-342. 

McNabb, D. E. (2010). Research methods for political science. Thousand Oaks, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Schneider, D., Rodgers, Y. v. d. M., & Cheang, J. M. (2008). Local government coordination of community food systems in distressed urban areas. Journal of Poverty, 11(4), 45-69. 

Yin, R. K. (2009). Case study research: Design and methods. Thousand Oaks, CA: Sage.