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Economists Interpret Non-Farm Employment Data Discrepancies: ADP Growth Set to Slow Significantly
Independent macroeconomic research firm Continuum Economics’ senior North American economist Dave Sloan recently published a new analysis revealing systematic differences between non-farm payrolls and ADP employment data. According to his forecast, the gap between these two employment indicators will further widen, reflecting the complex dynamics of the current U.S. labor market.
Why Is There a Discrepancy Between ADP Data and Non-Farm Payrolls?
Data comparison shows that ADP employment is expected to increase by 30,000 in January, slowing down from December’s 41,000, a decrease of about 27%. Meanwhile, non-farm payrolls are projected to grow by 85,000, significantly higher than ADP figures. The gap between the two is expected to expand to 50,000, marking a recent notable deviation.
Sloan points out that this discrepancy is not accidental. Over the past six months, data shows that ADP figures have been on average 22,000 lower than non-farm payrolls. Although the gap narrowed in September and November, the overall trend still indicates weaker performance in ADP data. Recent softness in the retail sector has had a significant impact on non-farm employment, increasing workforce mobility, while this factor has limited influence on the more focused ADP sample.
Sector Divergence: Construction Rebounds While Service Sector Remains Weak
Looking at detailed data, the growth in non-farm payrolls is mainly driven by a recovery in goods-producing sectors, especially signs of improvement in construction. However, employment growth in the service sector has slowed, acting as a drag on the overall figures.
Notably, the discrepancy between ADP and non-farm payrolls is most pronounced in the service industry. The education and healthcare sectors show particularly clear divergence, reflecting differences in data collection methods and sample composition. This suggests that investors and policymakers need to evaluate service sector employment quality through multiple data sources and cross-validation, rather than relying solely on a single indicator.