The neglected intersection between poverty and privacy in the United States

by Pam Dixon, Founder and Executive Director, WPF

WPF is pleased to announce a new project examining the intersection between poverty and privacy in the United States.

In the United States, the prevailing discussions about privacy rarely contemplate the poor, or how — or where — the poor or financially stressed may experience privacy challenges. This is also true of many legislative discussions regarding data governance, data protection, and privacy; there is generally not routine scrutiny of the intersection and impacts of proposed statutory language or approaches regarding those who live at or below the poverty level.

Poverty is too often misunderstood as something that other parts of the world suffers from, not the U.S. [1] Nevertheless, the data tells a different story: an unwelcome pattern is emerging from the data regarding how privacy and poverty intersect; that is, there is a distinct fiscal component to privacy. This can be seen in a number of ways, including in some specific use cases, where geographic areas with more fiscal capacity for implementation of privacy regulations tend to have better privacy practices on the ground. In practice, this disparity means that people living in poverty (or in poverty zones) may experience less privacy protections in comparison with others who have more financial capacity, or who live in wealthier cities, counties, or states. 

One place that WPF’s work has documented this issue is in the educational sector. In April 2020, WPF published extensive longitudinal research regarding how the Family Educational Rights and Privacy Act (FERPA) was being implemented at more than 5,000 elementary, secondary, and post-secondary schools in the United States. [2] In this four-year study, a troubling finding emerged from the data. That is, students who live in wealthier school districts had measurably better privacy, based on the analysis of implementation of FERPA. The methodology for the study balanced analysis among school districts across multiple variables, including a fiscal dimension. For the question “Does the financial context of a school have an impact on FERPA implementation?”  the data indicated that yes, in this study, financial context made a measurable difference regarding how FERPA was implemented.   

Data governance, privacy, and poverty are each multifactorial, complex issue areas which require nuanced approaches and solutions — legislative and otherwise. This intersection creates significant challenges, ones that have yet to be fully explored and articulated. There are a few things we know right now. First, one known contributing factor is the cost of privacy implementation and compliance. The high cost of compliance with some state-level privacy laws in the U.S. is fairly well-documented at this point [3]. Another factor is the urban-rural divide. For at least for one Federal privacy law, FERPA, there is WPF’s documentation of implementation disparities based on an urban / rural analysis. The particularities of geographically clustered economic divides are well-documented in the broader literature on poverty in the U.S. [4]

Another important factor is how individuals, households, and communities experience the intersection between poverty and privacy. Regrettably, much about the intersection of privacy and community / household / individual level poverty is not yet well studied. There is some harrowing data regarding the rates of poverty in the U.S., including data about the rates of homelessness among children who attend K-12 schools. [5]  This data is important, however, it does not fully allow us to understand and disentangle the interactions between poverty and privacy. This is in part because there is not enough emphasis on requirements for collecting data on the effectiveness of privacy regulations in the United States, leaving regulators and the public blind as to impacts unless independent studies are completed. 

To address these data and policy gaps, The World Privacy Forum has launched a project as of Spring 2022 to study and document the intersection between privacy and poverty, and to address what we find. Our first article examining this issue is already completed and will be published by the American Bar Association (forthcoming). [6] In addition, we will be holding three roundtables in the coming months on this issue, and will be reaching out to core stakeholders for input on challenges, gaps, and solutions. WPF welcomes inputs from all who are interested in participating. 


[1] Poverty is defined by UNICEF as living in a household that earns less than 50% of the national median.

[2] Pam Dixon, Bob Gellman, John Emerson, Without Consent: An analysis of student directory information practices in U.S. schools, and impacts on privacy. World Privacy Forum, 15 April 2020.

[3 ] Aly McDevitt, CCPA Compliance costs expected to reach $55B, Compliance Week, January 2020.

[4] Jelavich, Mark, U.S. Urban-Rural Income Differences: A 2019 State-Level Study (March 13, 2022). . Jelavich’s study on poverty rates and impacts in U.S. rural (non-Metropolitan Statistical Areas) areas in comparison to urban areas regarding per capita personal incomes, unemployment, and poverty rates reveals important underlying patterns which are in line with the findings of disparities in WPF’s work in Without Consent. There is a significant body of existing work regarding the geography of poverty. See: Scott Allard, Places in Need: The changing geography of poverty, New York: Russel Sage Foundation. . See also research regarding spatial clustering of poverty by Katherine J. Curtis, Junho Lee, Heather A. O’Connell and Jun Zhu, The Spatial Distribution of Poverty and the Long Reach of Industrial Makeup of Places: New Evidence on Spatial and Temporal Regimes, Rural Sociology, 2019, Vol. 84, no. 1, pp. 28-65. . 

[5] Digest of Education Statistics, NCES. Available at:

[6] Pam Dixon, In Theory Only: The ugly intersection between poverty and privacy in America. American Bar Association Spring 2022 Compendium, forthcoming.