With the upcoming release of the September US nonfarm payroll (NFP) data, market participants are bracing for potential surprises. The complexities of measurement issues in initial payroll prints have often left economists grappling with data interpretations, and September’s release appears no different. The forecast range for this month’s payrolls suggests an unusually wide band of plausible outcomes.

Fundamental Insights and Anomalies

Economists are pointing to a mixture of underlying fundamentals and a month-specific anomaly, which signals a potential increase of about 110,000 jobs. However, the real shocker could come from statistical adjustments. These factors suggest that as many as 300,000 net jobs might have been added last month, a far cry from initial expectations. These discrepancies highlight how difficult it has become to rely on conventional forecasting methods, making this month’s release even more unpredictable.

Consensus and Baseline Forecasts

Many alternative high-frequency data sources are pointing to a number that is more likely to overshoot the consensus forecast of 150,000 jobs. Goldman Sachs’ baseline forecast expects a 188,000 increase in jobs, a middle-ground estimate with the possibility of a much larger print.

How the S&P Might React

Depending on how the headline payroll number lands, the market response could vary significantly. Historically, the S&P 500’s reaction to payroll prints can be mapped out as follows:

• If the print exceeds 200k: The S&P is likely to sell off, falling 0-50 basis points (bps).
• For prints between 150k-200k: The market typically rallies by 100 bps.
• For prints between 100k-150k: Market reaction is more muted, either rising or falling by around 50 bps.
• If the print falls below 100k: Expect a more severe market response, with a selloff of at least 100 bps.

This tiered reaction framework gives investors insight into how sensitive the S&P 500 may be to the September payrolls outcome, with the potential for significant volatility depending on the final data.