The Skeleton of Our Ideology: How to See the Truth When Economic Data Lies

The Skeleton of Our Ideology: How to See the Truth When Economic Data Lies: An analysis of politically-corrupted data and a blueprint for a more honest future. 

To Economic Scholars, Data Architects, and Policy Modelers:

As we confront the aftershocks of 2025’s structural ruptures—amid widespread federal workforce purges, trade wars, and judicial retrenchment—it is imperative that we reassess the economic databases and interpretive models upon which our discipline relies. I write to emphasize the transformative implications of Public Health Liberation (PHL) theory for economic data infrastructure, especially in light of recent empirical volatility under the Trump administration.

PHL theory, articulated by Williams (2025), contends that our traditional frameworks—centered on price, utility, and institutional neutrality—obscure the morally entangled, factional, and historically contingent nature of economic life. That critique extends directly to the content, structure, and epistemic assumptions of our datasets.

I. Disrupted Data Inputs: The Fiction of Continuity

2025 Trump-era shocks—including the “Liberation Day” tariffs, court-sanctioned purging of federal scientists, and suspension of public health grants—have compromised the longitudinal continuity of macroeconomic indicators, particularly:

• Import/export volumes are now decoupled from historical trend models due to retaliatory tariff shocks (e.g., 145% Chinese goods tariff).

• Agency-level spending data (NIH, CDC, EPA) no longer reflect pre-2025 baseline capacity, given over 275,000 layoffs and defunding of regulatory bodies.

• Grant disbursement flows (especially to marginalized-serving institutions) were abruptly paused or nullified, distorting sectoral input-output tables.

→ PHL implication: These events are not “noise” to be adjusted for—they are structural interventions with psychosocial and distributional significance. Data inputs that ignore agency degradation and racialized disinvestment are epistemically fraudulent.

II. Interpretation Errors: Misreading the Moral Economy

Economists often misinterpret GDP dips or CPI anomalies as market inefficiencies or stochastic variation. PHL reorients us toward moral interpretation:

• The removal of birthright citizenship and chilling effects on immigrant labor create a hidden demographic contraction not fully captured in labor market data.

• Judicial rollbacks (e.g., affirmative action bans) alter not just educational access but the future earnings trajectories of entire racial cohorts—data effects that are long-lagging and often uncaptured.

→ PHL implication: Our models must evolve from mechanistic interpretation to structural diagnostics. Equity-denying laws are not policy variables—they are economic shocks with intergenerational cost curves.

III. Asset Valuation & Investment Mispricing

The assumption that asset value reflects discounted future cash flows is undermined when health, climate, and legal determinants are politically destabilized.

• Hospital REITs and biotech equities rose in 2025 due to deregulation and privatization—but PHL theory warns this masks rent extraction from structurally silenced populations, e.g., the uninsured post-Medicaid clawbacks.

• Green energy and ESG-aligned firms suffered from administrative hostility and grant defunding—creating a policy risk premium unaccounted for in CAPM or Black-Scholes assumptions.

• University endowments and urban infrastructure portfolios may be overvalued due to underestimation of affirmative action rollbacks, which will reduce elite diversity pipelines and increase social volatility.

→ PHL implication: Standard asset pricing fails to account for liberation-constraining events as material risk factors. Future valuation models must build in structural justice coefficients to remain predictive.

IV. Investment Decision-Making: Failing the Moral Forecast

Institutional investors and public policy allocators rely heavily on data dashboards that track cost-benefit ratios, impact multipliers, and utility-based scoring. These tools are now ethically and methodologically compromised:

• 2025’s mass layoff of federal data scientists (BEA, BLS, NIH) created statistical blind spots in health sector modeling.

• New Treasury algorithms to rank “efficient” funding recipients downgraded historically Black colleges, tribal systems, and liberation-focused initiatives—creating anti-equity capital flows with long-term destabilization potential.

→ PHL implication: PHL argues that decision frameworks must shift from efficiency-based justification to constraint-exposing diagnostics. The failure to fund liberation efforts is not a neutral allocation—it’s an active reproduction of economic illiberation.

V. Redesigning Economic Databases: PHL-Informed Proposals

To adjust for these limitations, I propose five critical PHL-aligned database modifications:

Feature - Current Status vs PHL-Aligned Enhancement

Labor Data - Aggregate by sector/race  vs Integrate illiberation scores (fear, silence, exposure) by cohort

Grants & Aid - Tracked by dollar flow  vs Add flags for structural disempowerment: e.g., aid interruption, population exclusion

Legal Events - Often excluded  vs Encode judicial determinants of health (e.g., court decisions) as economic shocks

Policy Volatility - Narratively noted  vs Quantify policy trauma index to track psychic and investment disruption

Asset Valuation- Based on cash flow vs Create Liberation Risk Factor—a pricing adjustment for inequality-producing events

Conclusion: Liberation as Economic Data Imperative

The 2025 Trump administration did not simply change policy—it revealed the fragility and political contingency of our economic data ecosystems. Public Health Liberation theory exposes the deeper reality: that metrics devoid of moral architecture will continue to misprice risk, misallocate capital, and misinterpret suffering.

PHL insists that we center liberation—not optimization—as the evaluative axis. This shift must begin with our databases, for the structure of our data is the skeleton of our ideology.