an introduction to nonfarm payrolls a presentation on behalf of the cme group raymond stone stone...

An Introduction to Nonfarm Payrolls

A Presentation on Behalf of the CME Group

Raymond Stone

Stone & McCarthy Research Associates

April 9, 2008

Research has revealed that the U.S. Nonfarm Payroll Numbers have the most profound impact of all high frequency economic reports on not only the U.S. debt markets, but also upon foreign sovereign bond markets.

• See: What Moves Sovereign Bond Markets?The Effects of Economic News on U.S. and German Yields , (Goldberg and Leonard) Current Issues in Economics and Finance, September 2003, Federal Reserve Bank of New York http://www.newyorkfed.org/research/current_issues/ci9-9/ci9-9.html

Actually the monthly Employment Report includes data from two separate surveys:

• Household Survey—A survey of roundly 60,000 households. The “Unemployment Rate” is compiled from this survey.

• Establishment Survey (or Payroll Survey)—Captures payroll counts from roundly 330,000 business and governmental establishments. From this survey the monthly change in Nonfarm Payrolls is estimated.

Why do markets place such enormous

emphasis on the payroll data?

• Timing —usually released the first Friday of the month, capturing employment conditions of the prior month.

• Comprehensive — providing significant industry specific detail regarding payrolls, hours worked, and earnings.

Payroll data serve as important inputs into other key macroeconomic data.

• Personal Income—Wages & Salaries, the largest component of PI, are derived from the earnings data from the establishment survey.

• Industrial Production—Production-worker hours account for nearly ½ of the input to the monthly IP report.

• Productivity—Hours worked serve as the denominator in the Productivity equation: Productivity = Output/Hours

• Unit Labor Costs—Earnings serve as the basis for computing compensation costs.

More Generally, Payrolls Tend to be Aligned with other Measures of Macro-activity

How Does the BLS Estimate Payrolls?The three components:

• (1) Sample-Based Estimate

• (2) Births/Deaths Estimate

• (3) Seasonal Adjustment Estimate

Sample-Based Estimate

• Sample of roundly 300,000 private sector and 30,000 public sector establishments.

• Survey is based on the pay-period that includes the 12th of the month.

• Only respondents that report in consecutive months are included.

• Typical response rate for the first estimate of payrolls is around 66%.

More than ½ of the responses are collected by touch-tone data entry (TDE) or via computer assisted telephone interviews (CATI).

The BLS uses a “Probability-Based Sample” to correct for differences between the sample and the universe. For example, small firms are under-represented,

but BLS corrects for this by assigning higher weights to these firms.

Payroll survey is skewed towards larger firms

Births/Deaths Model (BDM)

• The BLS’s sample cannot account for new firms (Births) or firms going out of business (Deaths).

• As a consequence, the sample-based estimate of payrolls would typically understate payroll growth.

• To account for this shortfall the BLS employs a time-series model to provide current estimates of net payroll changes associated with Births and Deaths of businesses

The BDM tends to render a recurring pattern of estimated net payroll changes associated with Births and Deaths of firms—The so-called “BDM adjustment” is added to the sample-based estimate of payrolls

Because the BDM is a “Times-Series Model” it doesn’t do well in picking up turns or inflection points in the underlying series.

• During economic downturns or slowdowns the BDM adjustments tend to add too many jobs

• During periods of economic acceleration the BDM adjustment may understate net new jobs associated with Births and Deaths of firms

Once a year the BLS realigns its monthly estimate to a less timely but more comprehensive accounting of payrolls (unemployment insurance records)..This is the “Benchmark Revision”

Seasonally Adjusting The Payroll DataNot An Easy Task

Another Estimate of Payrolls ADP National Employment Report (issued the Wednesday before the BLS release)

Approaches Used to Forecast Payrolls

• Model-Based approach—recession models using Initial Unemployment Claims, ISM Employment Indices, etc as independent variables

• Naïve or Adaptive approach—typically based on the extension of recent trends: looking at moving averages, etc.

• Judgmental—combines model based, adaptive, weather considerations, and a variety of other factors in developing a payroll forecast

Evolution of Market Expectations

• Estimates of the next payroll release begin to surface about 2 weeks prior to the report.

• Estimates tend to be revised based on incoming data, including the Thursday release of Initial Unemployment Insurance Claims.

• Estimates are further revised by the Employment Indexes from the ISM Manufacturing (1st business day of month) and the ISM Non-manufacturing reports (3rd business day).

• The ADP National Employment Report (Wednesday prior to BLS release) often tends to either reshape or reaffirm market estimates.

Forecasters do better in some months than others

Over the past 10-years Forecasters have mostly over-shot Payrolls

About 2/3rds of all Forecasting Errors are within + or – 100,000

Often Weather Related Swings Account for Forecasting Errors

Forecasting Errors Exhibit a Pattern Similar to the Business Cycle

Some Forecasters Do Better Than Others, But Over Time the Median Forecast Has Out-Performed All Forecasters in the Sample