LIVE MARKETS-Why noisy economic data could cost the economy

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WHY NOISY ECONOMIC DATA COULD COST THE ECONOMY

Faulty economic data isn't just a headache for policymakers — it can be costly when it comes to assessing the health of an economy as well.

Goldman Sachs estimates that if noisy statistics lead to the central bank making the wrong move, such as hiking rates when it shouldn't or delaying a cut, the GDP hit could reach 0.1% over two years, even if the error is corrected within a quarter. That's more than the annual budget of most statistical agencies.

Concerns about data reliability have grown after a string of high-profile missteps at statistical offices worldwide, worries that deepened when President Donald Trump fired BLS chief Erika McEntarfer earlier this month following a weak July jobs report and steep downward revisions to May and June payrolls.
Goldman points to three drivers of weaker data quality: post-pandemic volatility that distorts seasonal patterns, stagnant funding for statistical agencies, and a sharp fall in survey response rates since COVID-19.
Evidence of actual deterioration in global economic data is mixed, Goldman says, but discrepancies between household and payroll employment measures have widened, standard errors in releases have risen, and market surprises are increasingly out of step with underlying economic activity.
The BLS revised May and June payrolls down by a combined 258,000 jobs were the biggest non-COVID-era two-month drop since the 1980s, analysts at Standard Chartered note. They add that first-release payroll estimates now capture responses from just over 40% of surveyed firms, down from about 60% pre-pandemic.
That leaves early readings vulnerable to sampling bias, which historically tends to overstate job growth during downturns.
For central banks like the Federal Reserve, which lean heavily on preliminary labor data, this bias can mean policy lags and sharper course corrections later.

Both Goldman and StanChart suggest giving more weight to revised datasets and bolstering statistical capacity to ensure policy decisions are built on the firmest possible footing.

(Joel Jose)

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