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Explaining disparities in food safety compliance by food stores: does community matter?

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Abstract

This paper provides a conceptual framework to explain why disparities may exist in food safety code compliance by food stores in different neighborhoods. Explanations include market dynamics, community characteristics, retailer attributes, inspector characteristics, and enforcement approaches, and interactions among the factors. A preliminary and limited empirical test of some of these relationships in Detroit, Michigan shows a higher rate of food safety violations by stores in poorer neighborhoods and in neighborhoods with higher concentrations of African-American residents. Stores inspected by female inspectors also scored higher numbers of critical violations, suggesting a need for greater examination of the social relations associated with enforcement interactions in food safety studies.

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Notes

  1. Community food security is defined as a situation in which all community residents have access to a safe, culturally-acceptable, and nutritionally-adequate diet through a sustainable food system that maximizes self-reliance and social justice (Hamm and Bellows 2003).

  2. For this reason, many states have implemented a monthly pattern of staggered disbursement of supplemental nutrition funds; there is some suggestion that Michigan may move in that direction in response to recent policy advocacy (Klinefelter 2006). An additional note: although the term “food stamps” is commonly used, Food Stamp Program benefits are now provided through a monthly “Electronic Benefits Transfer” (EBT) to participants’ accounts. Michigan Food Stamp Program participants use their EBT card, or “Bridge Card” as it is commonly known, as a debit card when they purchase groceries.

  3. Marsden et al. (2000) emphasize that reliance on corporate self-regulation does not mean that the public sector has ceded its traditional role; rather, roles are now distributed differently in ways that are intimately linked to the maintenance of markets for corporate retailers. This study suggests that even independent retailers may have a degree of influence over food safety enforcement, via their trade associations. Despite a higher rate of critical violations in some Detroit stores, store closures are very rare, and fines and other sanctions imposed against habitual violators tend to be quite modest. A first offense (such as the finding during a follow-up inspection that a critical violation observed during a regular inspection has not been corrected) may cost the operator $100; a second, $150, etc. (Phone interview, Gerald Wojtala, Deputy Director, Food and Dairy Division, Michigan Department of Agriculture, 5 January 2007).

  4. Jacobs (2003) writes that operators need to successfully follow a 7-step approach to be seen in a positive light by inspectors: (a) conduct a hazard analysis, (b) determine critical control points, (c) establish critical limits, (d) establish monitoring procedures, (e) establish correction procedures, (f) establish verification procedures, and (g) establish recordkeeping and documentation procedures.

  5. Two limited-assortment grocery stores of Germany-based discount chain Aldi’s, and three stores of Save-A-Lot, operated by licensees of Minneapolis-based food distributor SuperValu, exist in Detroit. These are smaller stores than those operated by national chains present in Southeastern Michigan (but not Detroit), such as Kroger or Super Wal-Mart. Regional leaders such as Meijer’s have no stores in Detroit. Finally, 35 of Detroit’s independent grocers are sourced by Spartan Stores, a supplier based in Grand Rapids, MI.

  6. For example, in the early 1990s, the Harmony Project, an initiative led by Wayne State University’s Center for Peace and Conflict Studies, brought together neighborhood residents and convenience store operators in Northwest Detroit to raise and resolve mutual grievances, promote greater understanding of each side’s experiences and perceptions, and provide a channel for ongoing communications (Pothukuchi 2005b).

  7. The data were originally requested by the Detroit Free Press under the Freedom of Information Act (FOIA). We obtained them through yet another FOIA request. The Free Press requested inspection reports for grocery stores selling a variety of fresh, prepared, and ready-to-eat food products; this exact category of stores was also of interest to us.

  8. Only records generated following a routine inspection of stores were included in this study. Inspections undertaken to verify a customer complaint were excluded from consideration due to their non-standard inspection protocol and because complaints could turn out, upon investigation, to be legitimate or baseless in unpredictable ways.

  9. Additionally, operators may cooperate with each other in purchasing and warehousing, and in other areas, through membership in one of two trade groups in the Detroit area.

  10. The primary author’s many attempts to interview several Detroit store operators, especially of Chaldean-American ethnicity, were unsuccessful. Phone and written requests to the MDA’s Detroit regional office (31 May 2007), to interview inspectors of Detroit area stores also yielded negative responses from Louis Ognjanowski, Regional Field Supervisor. Mr. Ognjanowski forwarded our request to the main MDA Food and Dairy Division office in the state’s capital, Lansing. A conference call initiated on 12 June 2007 by the department’s public relations associate, Jennifer Holton, included Katherine Fedder, Director of MDA Food and Dairy Division, and Kevin Besey, Food Service Sanitation Section Manager, and led to a denial, yet again, of permission to interview inspectors. Ms. Fedder blamed an executive order by the Governor, which restricted discretionary activities of inspectors, including training and participation in research. When asked for her views on the differences in rates of violations between Detroit stores and stores in the rest of Michigan, she, like Mr. Ognjanowski before her, underscored the standard and professional nature of protocols used by inspectors, and primarily identified the older infrastructure from which Detroit stores operate as reasons for more violations there. When pushed, she acknowledged other factors but did not name any.

  11. Unlike OLS regressions where the coefficient is also the marginal effect, the Tobit coefficients need to be modified to determine the marginal effects. This issue arises because although there are two distinct cases in the sample—the censored zero values and the uncensored non-zero values—the Tobit analysis provides only a single coefficient (see McDonald and Moffit 1980; Roncek 1992). The marginal effect of each independent variable is the product the Tobit coefficient and the proportion of uncensored observations. This marginal effect can be further decomposed into two components (McDonald and Moffitt 1980). The first component is the marginal effect of the independent variables on the number of critical violations, given that a store has a critical violation. This is given by the equation \( \frac{{\partial Ey^{ * } }} {{\partial X_{i} }} = \beta _{i} \times {\left[ {1 - {\left( {z \times \frac{{f(z)}} {{F(z)}}} \right)} - \frac{{f(z)^{2} }} {{F(z)^{2} }}} \right]} \), where \( \frac{{\partial Ey^{ * } }} {{\partial X_{i} }} \) is the marginal increase in critical violations, given that the store has experienced a critical violation; \( \beta _{i} \) is the parameter estimate produced by the Tobit regression; \( F(z) \) is the cumulative normal density function, usually taken as the percentage of uncensored observations; \( z \) is a on the normal distribution; and \( f(z) \) is the associated normal density. The second component provides a measure of the probability that observations without critical violations would experience such a violation. The equation is given by \( \frac{{\partial F(z)}} {{\partial X_{i} }} = \beta _{i} \times \frac{{f(z)}} {\sigma } \), where \( \frac{{\partial F(z)}} {{\partial X_{i} }} \) is the probability that a store with no critical violations would experience a critical violation, \( \sigma \) is the standard error of the regression, and the other terms are the same as above.

  12. The 1999 Food Code and other documents used by the MDA discuss differences between critical and non-critical violations. For example, a critical violation occurs when an employee with poor hand-washing practices processes raw cabbage that will be used to prepare coleslaw. The risk here emerges from the fact that there is no “kill” step for potential pathogens. On the other hand, an employee using her bare hand to scoop flour out of a bin for subsequent baking would face a non-critical violation. Critical violations need to be corrected immediately, but in some cases this may not be possible. The inspector may allow up to 10 days for a critical violation to be corrected. The inspector also may ask for the cessation, limiting, or slight relocation of some operations to prevent a hazardous food safety occurrence while the critical violation is being corrected.

  13. We explored the use of Tobit regressions for TV. In terms of which variables are statistically significant and the point at which they are significant, the OLS and Tobit regression are identical. In terms of the magnitude of the coefficients, the OLS and Tobit regressions are only slightly different. This lends support to our decision to use OLS regressions for examining TV.

Abbreviations

CVs:

Critical violations

FDA:

US Food and Drug Administration

FOIA:

Freedom of Information Act

HACCP:

Hazard analysis and critical control point

MDA:

Michigan Department of Agriculture

OLS:

Ordinary least squares regression (a statistical technique)

TVs:

Total violations (Sum of critical and non-critical violations for a particular store)

WIC:

Women, Infants, and Children (Special Supplemental Nutrition Program)

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Acknowledgements

This paper was researched and developed during a faculty fellowship of the primary author at Michigan State University’s Global Urban Studies Program during her sabbatical year. She is grateful for the support she received from the program. The authors thank Professors Toby Ten Eyck and Lawrence Busch, Michigan State University, and David I. Gilliland, Colorado State University, for their valuable insights during the production of this paper, and the journal’s editor and two anonymous reviewers for their insightful comments on an earlier draft. Any remaining errors, however, belong solely to the paper’s authors. We are also grateful to Gerald Wojtala, Deputy Director of MDA’s Food and Dairy Division, for his timely and valuable assistance in providing data and responding to questions. Rayman Mohamed would like to thank the Department of Geography and Urban Planning at Wayne State University for time release to work on the paper.

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Authors and Affiliations

  1. Department of Geography and Urban Planning, Wayne State University, 3207 Faculty Administration Building, Detroit, MI, 48202, USA

    Kameshwari Pothukuchi & Rayman Mohamed

  2. Department of Agricultural Economics and Global Urban Studies Program, Michigan State University, East Lansing, MI, USA

    David A. Gebben

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  1. Kameshwari Pothukuchi
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Correspondence to Kameshwari Pothukuchi.

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Pothukuchi, K., Mohamed, R. & Gebben, D.A. Explaining disparities in food safety compliance by food stores: does community matter?. Agric Hum Values 25, 319–332 (2008). https://doi.org/10.1007/s10460-008-9132-2

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