Earnings Call, Webinar
How Reliable is Economic Data?
- The U.S. payroll survey coverage is projected to rise from approximately two-thirds in the first month to 94%–95% by the third closing date, with revisions continuing as late reports are integrated.
- Potential economic turning points, such as recessions, may cause extreme events to delay reporting, introducing a negative bias in late data revisions.
- Budgetary constraints and staffing reductions at statistical agencies are expected to reduce non-response follow-up efforts, leading to smaller sample sizes and reduced granularity in price indices.
- If staffing levels remain unchanged, the Bureau of Labor Statistics (BLS) is likely to reduce its operational scope, eliminate specific programs, and cut back on activities.
- A proposal to convert monthly economic data reporting to quarterly intervals is anticipated to deprive policymakers and the public of real-time information.
- The standard error for the U.S. JOLTS survey is forecast to remain approximately 80% higher than the 2002–2013 baseline due to sharp contractions in firm response rates.
- Monthly Consumer Price Index (CPI) figures are expected to see their standard error approximately double as a result of budget-driven data collection cutbacks.
- Standard errors for the retail sales survey are likely to increase in both Australia and the U.K. due to declining response rates.
- Divergence between household and payroll employment measures in the U.S. and U.K. may persist as datasets potentially lose the ability to measure underlying economic activity with sufficient appropriateness.
- Personnel changes aimed at replacing career civil servants or converting roles to politically fireable positions under "Schedule P" pose risks to statistical integrity and agency culture.
- While some anticipate that personnel changes and structural adjustments at the BLS may correct past mistakes, concerns exist that unrelied in economic data could lead to a collapse in trust and a lack of lending willingness.
- Once trust in official statistics is lost, recovery is historically observed to take a very long time, as evidenced by the destruction of credibility in Argentina's statistical agencies.