Equity Through Data?
Author: Matisha Montgomery
Equity is promoting justice, impartiality, and fairness within the procedures, processes, and distribution of resources by institutions or systems.1
We’ve all had a front row seat while numerous events in the last two years have underscored the long-standing systemic inequities that exist in the United States and abroad. Underrepresented populations continue to face disparities in all systems including healthcare, criminal justice, education, and economy. Addressing these disparities requires an understanding of the deeply rooted causes, and my hope is that data can serve as the impartial key to unlocking this understanding. In the Federal Government, we seek to leverage data to analyze, model, and objectively measure equity outcomes in programs and policy decisions. However, challenges exist in the data.
Missing: Challenges with Data Collection and Management2
Data is frequently not collected at the level of detail necessary to evaluate equity outcomes. The collection and disaggregation of data relating to underserved communities (e.g., race, ethnicity, sex, disability status, gender identity, and sexual orientation) are inconsistent or missing altogether. The lack of consistency exists because this data is either not collected, is unreliable (voluntary but not verified), or is incomplete. When missing data pertains to underserved groups, the needs of those groups remain unaddressed. Illustrations of the lack of sub-categorical, detailed data collection in workforce demographic data are routinely uncovered. Systems of record across the Federal Government are not configured to collect data beyond sex (male/female) completely removing an agency’s ability represent whole sections of the population. Moreover, systems have default settings to report male for sex and white for race rather than leave the fields blank thereby artificially enhancing the majority.
Hidden: Challenges with Privacy
The sharing, retention, and use of personally identifiable information (PII) across government programs, even when data collection reaches sufficient levels of granularity, is generally limited and strictly regulated, which creates challenges with data merging, management, and analysis. Data in the Federal Government is understandably safeguarded to protect individual privacy and ensure the data is not appropriated for unauthorized uses. Often these safety measures are imposed through statute but more often it is a result of internal agency or office policy driving the limitation. In an effort to protect data privacy and restrict use, agencies have instead generated stovepipes effectively limiting the data’s usage to only it’s intended purpose rather than allowing the data to be combined with other datasets to reveal meaningful analysis.
Executive Order on Advancing Diversity, Equity, Inclusion, and Accessibility in the Federal Government3
The current Administration is countering systemic inequity by ensuring all policies include principles and approaches that remedy inequities and promote equitable outcomes and that the Federal Government’s workforce will reflect the people it serves. The June 25, 2021 Executive Order on Advancing Diversity, Equity, Inclusion, and Accessibility in the Federal Government requires agencies to improve how demographic data is collected on Federal employees to drive data-driven and evidence-based approaches for reducing barriers in hiring, promotions, professional development, and retention practices.
But the Executive Order gives me “hope heartburn.” The Order offers a positive path forward (hope) but doesn’t necessarily acknowledge the hard work it will require to implement (heartburn). To fully support the Executive Order, the Federal Government needs human capital data standards preferably established through a DEIA lens. The Office of Personnel Management, Office of Management and Budget, the Chief Human Capital Officer (CHCO) and Chief Data Officer (CDO) Councils must work in close partnership for a whole of government approach and coordinate with the private sector and non-governmental organizations to model best practices. Consistency in data practices, structures, and standards established through collaboration would enable broader scaling and application. Collaboration across organizational lines could lead to sharing of data to draw deeper meaning and conclusions and uncover best practices.
Addressing systemic inequities in policies and programs is aspirational. The goal looks achievable if we use data to drive equitable outcomes. Further, the Executive Order provides an opportunity for collaboration in the collection, analysis, and sharing of data. Improved quantitative data, assessed with qualitative data obtained in consultation with stakeholders who have lived experiences, moves us much closer to understanding and addressing equity in the Federal workplace. A la Alexis Rose4, I love that journey for us!
(1) https://dei.extension.org/
(2) https://www.gao.gov/products/gao-21-67
(3) Executive Order 14035. (2021, June 25). “Executive Order on Diversity, Equity, Inclusion, and Accessibility in the Federal Workforce,” The White House. Available: https://www.whitehouse.gov/briefing-room/presidential-actions/2021/06/25/executive-order-on-diversity-equity-inclusion-and-accessibility-in-the-federal-workforce/
(4) https://en.wikipedia.org/wiki/Alexis_Rose
