Data Distrust: Declining Survey Participation Sparks Concern Over Government Statistics

Mark Twain’s timeless observation rings true: “There are three kinds of lies: lies, damned lies, and statistics.” Yet, the skepticism toward government-published statistics is not unfounded. Federal agencies frequently alter methodologies, complicating comparisons between datasets. Moreover, adjustments, syntheses, and revisions to data are common, potentially politicizing government data and rendering some methodologies obsolete in today’s economy.

Compounding these issues is the alarming decline in participation rates in government data collection surveys, especially evident post-COVID mandates. This decline undermines the reliability and utility of survey data, exacerbating existing concerns about its accuracy.

The reluctance to participate in government surveys stems from a fundamental distrust of government entities and their affiliates. Concerns about privacy, data security, and the potential weaponization of personal information discourage individuals and businesses from cooperating with government data collection efforts. Furthermore, businesses may be wary of sharing proprietary data that could compromise their competitive edge.

Declining response rates introduce biases into the data, undermining its credibility and reliability. In some cases, government agencies resort to offering financial incentives to encourage participation, further skewing the data.

Federal Reserve Chairman Jerome Powell’s repeated assertions about the Fed’s reliance on data for policy decisions are rendered dubious in light of the quality concerns surrounding government data. The lack of dependable data raises questions about the efficacy of policy decisions and the Fed’s ability to navigate America’s fiscal challenges effectively.

In a landscape where data integrity is increasingly questioned, individuals are urged to exercise independent judgment, scrutinize official data critically, and rely on their own research and intuition. The adage “garbage in, garbage out” underscores the importance of discerning the quality of data before drawing conclusions or making decisions.