Information is not only a resource, but the foundation for human dignity, self-determination, and democratic participation. In August 2025, President Donald Trump fired Erika McEntarfer, director of the Bureau of Labor Statistics, after the agency released revised employment figures showing weaker job growth than initially reported in previous months. Though revisions are a standard part of the reporting process, Trump alleged the data was deliberately manipulated to harm his political standing and make the economy look weaker (Katz, 2025; Murray 2025).

This intervention raises serious questions around public trust in the Bureau of Labor Statistics as a nonpartisan data institution, and about the integrity of information as a public good. As Julie Cohen argues in Right to Privacy (2013), privacy and information integrity provide the “breathing room” necessary for “critical subjectivity” and democratic self-government, and when these conditions are eroded, citizens become vulnerable to manipulation (Cohen, p.1911). 

Building on this, Jonathan van Geuns and Ana Brandusescu show how alternative data governance models such as data commons, trusts, and fiduciary frameworks, can shift power toward communities and safeguard information as a collective good. Taken together, these perspectives reveal that information must be protected not only as a human right, but also as a condition for democratic participation – requiring both transparent and accountable stewardship to resist manipulation.

Privacy, Truth, and Democracy

As Cohen argues, privacy and the integrity of information are not mere individual privileges, rather, they are structural conditions for democracy, providing the “breathing room” citizens need to develop “critical subjectivity” and resist manipulation (p.1906, 1911). Cohen’s notion of ‘breathing room’ refers to the protected space in which individuals can think, question, and develop autonomous perspectives. 

In practice, this means having access to reliable information free from manipulation or partisan pressure, allowing citizens to evaluate claims and participate meaningfully in public life. When data such as employment statistics releases are questioned or politicized, this breathing room shrinks, making citizens more vulnerable to misinformation and manipulation.

Manipulating or undermining systems of public information threatens democracy – without trustworthy information flows, citizens cannot engage critically or participate democratically.

When power reshapes information environments, citizens lose the capacity for critical thought and democratic participation – Cohen calls this a “modulated democracy,” under which surveillance and manipulation create citizens who are less critical and easier to control (p. 1912, 1918). In economic contexts, this can distort public discourse, erode trust in official statistics, and reduce the ability of citizens to hold leaders accountable.

Trump & the Bureau of Labor Statistics

The Bureau of Labor Statistics is a nonpartisan agency that collects a wide range of employment and price data and releases a monthly U.S. jobs report; revisions to jobs numbers are common, because the Bureau of Labor Statistics collects data on a rolling basis and based on seasonal adjustments (Murray, 2025). 

Data from institutions such as the Bureau of Labor Statistics are integral in policymaking, economic market activity, and public trust – how will the recent firing of the agency’s director impact the integrity of future Bureau of Labor Statistics reporting? What precedent does this set regarding other “nonpartisan” agencies? 

The dismissal of Bureau of Labor Statistics director Erika McEntarfer by Trump demonstrates how political interference in statistical reporting weaponizes doubt, undermining public trust in data and threatening democratic accountability. Drawing from Cohen, we can see this is an instance of information modulation that reshapes truth in an attempt to consolidate political control (p.1918).

Data Stewardship & Governance Frameworks

Van Geuns and Brandusescu’s exploration of data governance models demonstrate how structural stewardship can shift power away from centralized authorities to communities, and protect information as a collective right (p. 4). Different pathways for stewardship include data collectives, collaboratives, commons, and trusts, and encourage a transition away from profit-driven “big tech” strategies (p. 4, 7). 

In a data commons, for example, “data is pooled and shared as a common resource” to “address power imbalances by democratizing access to and availability of data” (p. 10). Another example, Indigenous data sovereignty, shifts control over data away from governments and directly into the hands of Indigenous Peoples (p. 16). 

The field is still emerging and there are few scalable examples of existing initiatives, but the potential for data governance is here (p. 7). These models illustrate potential structural safeguards – reforms that create the “breathing room” Cohen sees as essential – that could further insulate Bureau of Labor Statistics data from partisan manipulation by reinforcing accountability and collective benefit. 

Critical Citizenship & Feedback Loops

Recent controversy aside, the Bureau of Labor Statistics is a reputable agency: it puts great effort into securing public trust, and publishes detailed descriptions of its research methods and publishes a wide range of information that tell the story of how price changes and employment changes impact U.S. citizens (Stapleford, 2025). The agency expands upon this by supporting open data access and tools to the public via its website (BLS, Data Retrieval Tools). 

However, even the strongest structural protections are insufficient if citizens lack the skills to critically interpret information. Cohen suggests that diminished privacy and manipulated data shrink the capacity for democratic self-government, which makes education, participation, and engagement essential (p. 1911). 

By fostering media literacy and feedback loops between institutions and their publics, education can reinforce the resilience of democratic societies while complementing structural data stewardship, ensuring information truly functions as a human right. Feedback loops can take many forms: the public can create their own dashboards and interactive reporting – expanding on these mechanisms enable additional perspectives on the data and reinforce accountability (BLS, Dashboards). 

By creating avenues for public engagement, these mechanisms help ensure that the interpretation of data is transparent and accountable, rather than filtered through partisan or institutional biases.

Additionally, media literacy programs can teach citizens to interrogate data releases critically. For example, understanding how seasonal adjustments affect employment figures, or how headline numbers might obscure underlying trends. Such skills are crucial for resisting attempts to manipulate public perception and preserving the autonomy of democratic judgment.

Conclusion

The firing of the Bureau of Labor Statistics director underscores the fragility of institutions when political forces manipulate information flows. As Cohen reminds us, privacy and the integrity of information create the “breathing room” necessary for critical subjectivity, democratic participation, and resistance to the pressures of a modulated democracy (p. 1906, 1911). 

Van Geuns and Brandusescu offer complementary insights, showing that structural data governance models – including data commons, trusts, and collectives – can redistribute power, protect public information as a public good, and safeguard accountability (p. 4, 10). Together, these perspectives highlight that information is both a human right and a collective condition for democratic self-government. 

To preserve public trust and prevent the consolidation of power through information manipulation, institutions like the Bureau of Labor Statistics must remain transparent, insulated from partisan interference, and supported by both data stewardship and informed, engaged citizens. 

References

• Cohen, J. E. (n.d.). What Privacy Is For. Georgetown Law Faculty Publications and Other Works, 2526. https://scholarship.law.georgetown.edu/facpub/2526 

• Katz, E. (2025, August 6). “the neutrality of the agency has been eliminated”: Stats-based Feds Decry BLS chief’s firing. Government Executive. https://www.govexec.com/management/2025/08/neutrality-agency-has-been-eliminated-stats-based-feds-decry-bls-chiefs-firing/407272/ 

• Murray, I. (2025, August 5). Fact check: Trump’s claims jobless numbers were “rigged.” ABC News. https://abcnews.go.com/Politics/fact-check-trumps-claims-jobless-numbers-rigged/story?id=124353890 

• Stapleford, T. A. (2025, August 8). Bureau of Labor Statistics tells the US what’s up with the economy – trump firing its top official may undercut trust in its Data. The Conversation. https://theconversation.com/bureau-of-labor-statistics-tells-the-us-whats-up-with-the-economy-trump-firing-its-top-official-may-undercut-trust-in-its-data-262673 

• U.S. Bureau of Labor Statistics. (n.d.-a). Dashboards. U.S. Bureau of Labor Statistics. https://www.bls.gov/fls/dashboards.htm 

• U.S. Bureau of Labor Statistics. (n.d.-b). Data Retrieval Tools. U.S. Bureau of Labor Statistics. https://www.bls.gov/data/tools.htm 

• van Geuns, J., & Brandusescu, A. (2020). Shifting Power Through Data Governance. Mozilla Insights.