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Jobs Growth in April Was Even Better than the BLS Numbers Say

There are massive seasonal patterns in employment data. For example, in July, it is typical for the U.S. economy to lose over a million jobs. Adjusting for this normal seasonal variation is essential to interpreting month-to-month changes in employment. The approach for this seasonal adjustment that is presently used by the Bureau of Labor Statistics (BLS) puts very heavy weight on the current and last two years of data in assessing what are the typical patterns for each month.

Jonathan Wright of Johns Hopkins University argues in “Unseasonal Seasonals?” that a longer window should be used to estimate seasonal effects. He finds that using a different seasonal filter, known as the 3x9 filter, produces better results and more accurate forecasts. The key difference in the 3x9 filter is that it spreads weight over the most recent six years in estimating seasonal patterns. This makes the seasonal patterns more stable over time than in the current BLS seasonal adjustment method.

We calculate the month-over-month change in total nonfarm payrolls, seasonally adjusted by the 3x9 filter, for the most recent month. The corresponding data as published by the BLS are shown for comparison purposes. According to the alternative seasonal adjustments, the economy gained 304 thousand jobs last month. The Bureau of Labor Statistics reported that the economy gained 288 thousand jobs last month. The discrepancies between the two series are explained in Dr. Wright's BPEA paper.

Thousands of Jobs Added

BLS

Wright

2013-May

199

202

2013-June

201

177

2013-July

149

141

2013-August

202

205

2013-September

164

171

2013-October

237

222

2013-November

274

284

2013-December

84

90

2014-January

144

116

2014-February

222

249

2014-March

203

200

2014-April

288

304

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