Organizations working within food systems often assert that their work is centered around racial equity, but do not systematically track, report, or assess in ways that demonstrate whether they are enhancing racial equity through their work. Furthermore, we found that People of Color were often underrepresented in food systems organizations or at the multiple food summits hosted across Colorado. If, instead, organizations collected baseline data which accounted for race and tracked indicators that truly measure impact on communities of color, separate from economic disparities, food systems work could be better understood and refined in ways that actually improve the lived experiences of marginalized communities.
In this paper, we catalog select metrics that our research has found to be especially important to track for racial equity in Colorado’s food production sector. Many of the proposed metrics are associated with existing data sets; however, some metrics have been defined and vetted but do not have available data which would allow accurate measurements. Such data gaps can be detrimental for equity in the food system as it perpetuates invisibility and ignorance. One example of this is with agricultural workers; a group that government entities rarely, if ever, survey comprehensively, and therefore claim to have little proof of the realities and hardship they face in labor and life. Another example is when surveys are done that omit race, such as with membership of trade associations. Without data showing racial identity of trade organization membership, little can be claimed about the accessibility or equitable culture of these entities, and any claims that indicate exclusion or outright discrimination can be dismissed as unsupported claims. Such data gaps can be held up as a reason to resist policy and legislative change because the arguments in favor lack supporting evidence. Thus, the lack of data results in willful ignorance about the current conditions of equity.
Similarly, as political, social, and economic conditions change, metrics should change along with them to accurately and effectively measure the current contexts. For example, in 2000 the United Nations set out with a 15-year plan to achieve the Millenium Development Goals (MDGs), a set of eight goals with the first one being: “Eradicate extreme poverty and hunger”. This goal had targets (or KPIs) to halve the proportion of people living on less than $1.25 a day, and to halve the proportion of people who suffer from hunger. In the years leading up to the MDGs deadline, the UN began developing the next set of goals that would succeed and replace the MDGs for the next 15 year span: the Sustainable Development Goals (SDGs). The expanded 17 goals split poverty and hunger into separate categories, with separate targets (KPIs) and indicators (metrics) associated with each. This change in KPIs and metrics for tracking global development between 2000 and 2015 represent changes in both global contexts and the UN’s capacity to track more indicators and collect more data. We expect similar work will be needed for the equity metrics herein.
While this paper presents dense information and attempts to pull out specific metrics that can be used in analysis, readers should be wary of being locked in indecision. The intention of this paper is to help food systems advocates to see how these metrics can be helpful in both analysis of current conditions and for identifying goals that aim toward a more racially equitable system. Many of these metrics can be used in conjunction to build a systems-level understanding. We encourage readers to approach data gaps as a hurdle to overcome, not as a black-out space in knowledge. Rather than getting locked in indecision by data gaps, let this paper guide you through strategies for navigating partial data and provide inspiration for remedying missing data. In fact, isolated data points will never offer a full picture and readers are encouraged to examine some of this data in relationship and to incorporate qualitative data to develop a more robust further understanding.
This paper encourages readers to open up their minds and think differently about their own data capacities and the ways frontline community organizations can engage with data. While big data can be daunting to dive into, often through complicated query systems, non-intuitive dashboards, or enormous CSV files, it is not insurmountable. Data are intended to be used; they are intended to be referenced and analyzed. We can only explore shared data ownership and shared data governance models when more frontline and community-level advocates begin to see themselves as data scientists and become excited by what they can do with data to bring about their vision for a racially just system.
A last word of warning is to be tender and caring with the data that do exist. Do not approach these numbers as immovable facts, or as defining of a whole community. Data which represent people, should be treated with the same respect and civility as the people themselves. For example, current data indicate that Black producers are deeply underrepresented in Colorado’s agriculture sector, however we do not assume that will always be the case, and in fact hope to see this change over time. We will never define Black producers as underrepresented because we do not wish to cement that descriptor as a constant condition of the community. Instead, we ask readers to see the current conditions that Black, Indigenous, Asian, Hispanic and Mixed producers are faced with and to make moves to change those conditions - not the people. Furthermore, we warn readers to be extra careful in equating individuals to the numbers presented in this report. Communities of color are not monoliths and big data cannot be equated to individual experiences. For example, even though data indicate that Black producers receive the smallest share of monetary benefit from federal government programs, one should not assume that the singular Black producer in their community is in dire need of financial support. This is not the intended use of the data. Statistics and measurements can cause deep harm when applied to individual and relational interactions. These data and metrics are best used for large scale planning, change-making at the systemic level, and understanding where common struggles may be experienced across a community.
Acknowledgements
This paper is the result of almost four years of work. There have been many talented minds who have contributed to the advancement of this project during that time. University of Colorado, Boulder Masters of the Environment students Josh Yuen-Schat, Megan Paliwoda, Sarah Thorson, Jack Wertheimer, Elias Berbari, Hannah Wallace, Aspen Bias, Claudia Hall, and Zack Malley, who conducted background research under the direction of Fatuma Emmad and Kassandra Neiss, have enriched our work with their enthusiasm and innovative perspectives. This project would not have been possible without the generous support of Denver Food In Communities, The Colorado Health Foundation, and the Colorado Department of Health and Environment. Their commitment to advancing health equity has been crucial in bringing our research to life. We also thank our peer reviewers, Joël McClurg, Lucor Jordan, and Wendy Smittick, for their invaluable feedback and insights that have greatly contributed to the strength and integrity of our work. Together, we stand united in our mission to create a just, equitable, and sustainable food system for all.
Authors
By FrontLine Farming
Fatuma Emmad, FrontLine Farming Executive Director, Head Farmer and Co-Founder
Dr. Damien Thompson, FrontLine Farming Director of The Center and Co-Founder
Kassandra Neiss, FrontLine Farming Director of Research and Data Activism
Nicole Civita, FrontLine Farming Research and Development Consultant
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