How Reliable is Economic Data?

Episode Summary

Executive Summary: The episode examines rising concern over the reliability and politicization of U.S. and global economic statistics, especially payrolls and CPI, amid large revisions, declining response rates, and budget/staffing pressures at statistical agencies. Guests disagree on severity: some see real deterioration and institutional risk, while others say revisions are normal and the data remain broadly useful.

Main Topics: Why economic data reliability is under scrutiny (Priority: 5/5): The discussion opens with heightened attention on revisions, funding cuts, and the firing of the BLS commissioner, all of which have raised questions about whether official data still accurately reflects the economy. Structural causes of weaker data quality (Priority: 5/5): Joseph Briggs argues pandemic-induced volatility, stagnant statistical-agency funding, and long-run declines in survey response rates have made data harder to measure and more error-prone. How revisions work and why they happen (Priority: 5/5): Former BLS Commissioner Erica Groshen explains that revisions are a deliberate tradeoff between timeliness and accuracy, with late reporters often causing initial estimates to be revised. Budget and staffing strain at BLS (Priority: 4/5): Groshen warns that staff losses, hiring freezes, and vacant leadership posts reduce resilience, modernization capacity, and quality control, increasing operational risk over time. Debate over whether current deterioration is overstated (Priority: 4/5): Briggs says some indicators show genuine deterioration, but the evidence does not yet justify abandoning economic statistics altogether; revisions themselves have not clearly worsened broadly. Political independence and trust in statistical agencies (Priority: 5/5): The episode contrasts concerns about politicization with arguments that BLS processes are largely insulated from interference unless institutional safeguards themselves are weakened. Consequences of losing trust in official statistics (Priority: 5/5): Alberto Cavallo uses Argentina as a warning: once credibility is damaged, people assume the worst, markets react negatively, and trust can take years to rebuild.

Key Arguments: Large payroll revisions and weakened response rates have made data quality a live issue, especially around turning points in the economy. The pandemic disrupted seasonality and made sequential economic data harder to interpret. Statistical agencies have faced real capacity constraints because funding has not kept pace with the complexity of modern economic measurement. Revisions are a necessary feature of statistical production because early estimates trade off accuracy for timeliness. Initial payroll estimates are based on incomplete responses; late filings can materially change the picture, especially when extreme events hit firms unevenly. Recent revisions were unusually large and in the same direction, suggesting something beyond ordinary noise may be happening. BLS staffing cuts and vacant leadership positions may not yet have changed outcomes, but they weaken resilience, modernization, and quality control. Some data quality concerns are real, but the overall usefulness of economic data remains intact and not broadly in question. BLS methods and career-civil-service protections make direct political manipulation difficult unless institutional rules are changed. If official statistics lose credibility, the costs can be severe: distorted expectations, weaker policy decisions, and reduced investor confidence.

Data Points: BLS staff loss: 15% to 20%+ - Erica Groshen says BLS has lost at least 15% of staff, likely closer to 20%, with a hiring freeze still in place. Senior leadership vacancies: One-third - Groshen says one-third of BLS senior leadership positions are vacant. Payroll survey initial response rate: About two-thirds - Groshen explains the first payroll estimate typically captures about two-thirds of responses by the first closing date. Payroll survey final response rate: 94% to 95% - By the third closing date, the payroll survey usually reaches roughly 94%–95% response coverage. JOLTS response-rate standard error increase: About 80% higher - Briggs says the JOLTS survey's response-rate standard error is roughly 80% higher than during 2002–2013. CPI monthly standard error: About double - Briggs says CPI standard error is expected to roughly double because of collection cutbacks tied to budget pressures. Household and payroll divergence: Not quantified - Briggs notes divergence between household and payroll measures in the U.S. and UK as a sign of measurement strain. Argentina trust recovery: Many years - Cavallo says it took many years for trust in official statistics to recover after politicization in Argentina.

Pivotal Quotes: "Revisions are not bugs, they are features." — Erica Groshen: She explains why official labor statistics are revised over time and why timeliness and accuracy must be balanced. "The possibility that there will just be a screw up of some sort, weather-related, IT-related, whatever... that kind of thing." — Erica Groshen: She describes how staff cuts reduce resilience and increase operational risk at BLS. "People tend to assume the worst, and you end up with this asymmetric type of situation." — Alberto Cavallo: He describes how loss of trust in official statistics damages expectations and market behavior.

Implications: Listeners should expect more debate over data credibility, especially around jobs and inflation releases. The bigger risk is not a single bad print, but eroding institutional capacity and public trust, which can distort policy, markets, and long-run credibility.