) economist James O'Sullivan took a stab at quantifying the impacts. He looked at the historical relation-ship between data from several economic reports and deviations from the average December and January temp-eratures. December temperatures were used to capture any weather-related distortions that could carry over into January.
Based on these past relationships, the above-average January temperatures provided a 1.4-percentage-point boost to retail sales. Housing starts got a weather-related increase of approximately 200,000 units at an annualized rate, while the balmy temperatures may have accounted for all of the 0.7% rise in manufacturing output. Moreover, the UBS results show that the job growth number was about 69,000 higher than might have been expected had January temperatures been just average. The calculations are not meant to provide exact readings after accounting for weather distortions, cautions O'Sullivan, but it's clear the effects in some cases may have been large.The results show how the government's seasonal adjustment process, which tries to account for typical seasonal variation, can go awry when patterns are atypical. For example, the UBS report notes that the Labor Dept.'s seasonal adjustment expected a decline of 316,000 jobs. In reality, only 270,000 workers were let go, due to the warm weather. As a result, the seasonally adjusted gain was estimated at 46,000, far above the recent trend.
That process played out across much of the January data. One implication: Mild temperatures tend to bring forward a lot of activity that would have occurred later. As the weather reverts to the norm, February and March data may end up looking weaker than they would otherwise. By James Mehring in New York