Former BLS Commissioner Erica Groshen explains that data revisions are a designed feature, offering users a choice between fast but less precise initial data and slower but more accurate final data. It's an intentional balance between timeliness and accuracy.
Beyond budget cuts, a major threat to data reliability is a staffing crisis at the Bureau of Labor Statistics, where one-third of senior leadership positions are vacant. This loss of experienced personnel erodes institutional knowledge and resilience, increasing the risk of un-caught errors.
Declining real-term funding at agencies like the BLS creates a hidden cost. To ensure core reports are released on time, staff are pulled from long-term modernization projects, compromising the agency's ability to keep up with a changing economy.
Professor Alberto Caballo uses Argentina's experience to show that when citizens lose trust in official statistics, they tend to believe negative data but dismiss any positive reports as lies. This creates an economic environment where pessimism is entrenched and hard to reverse.
Large, negative revisions to economic data often occur around major economic turning points. This is because companies hit first by a downturn are more likely to delay reporting their data, which makes the initial economic reports appear stronger than reality.
Former BLS Commissioner Erica Groshen argues the agency's automated process makes it nearly impossible to manipulate a single report. The real danger is systemic change, like converting career civil servants into political appointees who can be fired, gradually eroding the agency's culture of impartiality.