Published Jul-27-2009
Civilian Noninstitutional Population: all persons 16 and older living in the 50 states and the District of Columbia who are not inmates of institutions or on active duty in the armed forces. This base population is used in the following definitions.
Civilian Labor Force: all persons in the civilian noninstitutional population classified as either employed or unemployed.
Not in the Civilian Labor Force: all persons 16 years and older who are neither classified as employed or unemployed. Examples include students, persons with disabilities who are unable to work, persons caring for their own homes and families, children or elderly, retirees, and those discouraged by job prospects.
Employed: all persons who, during the reference week (the Sunday through Saturday calendar week that includes the 12th day of each month), did any work as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as an unpaid worker in an enterprise operated by a member of their family, or were not working but who had jobs from which they were temporarily absent.
Unemployed: all persons who had no employment during the reference week, but were available for work, and had made specific efforts to find employment at some time during the four-week reference period ending with the reference week.
Unemployment Rate: the share of the civilian labor force that is unemployed, expressed as a percent.
Data collected from the CPS are used to produce labor force estimates directly for the United States and indirectly for the individual states and the District of Columbia. Nationally, there are some 60,000 eligible households in the sample. In Oregon, the sample size is around 1,000 assigned households.
Households in the CPS are initially selected using a probability design. A new sample of households is drawn after each decennial census. Basically, Oregon is divided into smaller geographical units, and households are selected within some of these geographical units to participate in CPS. Steps are taken to ensure the survey is reflective of the area's demographic and socioeconomic characteristics.
Part of the CPS sample changes each month. This is called the "4-8-4" sample rotation design, where a selected household is questioned for four consecutive months, dropped from the sample for eight months, and then returned to the sample and questioned for another four months. As a result, 75 percent of the households remain the same from month to month. Fifty percent of the households remain in the sample from the prior year. Once a household permanently leaves the sample, it is replaced by its neighbor to ensure that the survey remains reflective of the area's demographic and socioeconomic characteristics.
There are important characteristics of CPS survey error related to survey design. The sample overlap (4-8-4 rotation method) causes autocorrelated sampling error. The basic idea is that periods of overestimation and underestimation of the true value result from the way households are rotated in the survey. Since 75 percent of the households remain the same from month to month, a sample that is not representative of the population one month is likely to carry over into the next month.
Other factors that affect state CPS data reliability are changes to CPS design, sample size, and variation in labor force levels. These changes impact the size of CPS survey error. Thus, the magnitude of CPS survey error varies over time.
Graph 1 contains CPS unemployment rate data by month from January 1999 to the present. Graph 1 illustrates the important characteristics of CPS survey error. Wide month-to-month swings in CPS data make it difficult to discern real labor market changes from survey error, or "noise." For this reason, CPS data are not published. Econometric time series models are applied to state CPS data to minimize the impact of survey error on published labor force data for Oregon.
Many economic time series can be separated into components that explain trend, seasonal, and irregular patterns. The trend component represents movements in the data series that last more than a year, including the underlying upward or downward tendency of the time series. The trend component is also useful for identifying turning points in the series. The seasonal component represents patterns that last less than a year, such as short-run fluctuations caused by weather, holidays, and school schedules. The irregular component is what is left over after accounting for trend and seasonal components.
CPS labor force data contain trend, seasonal and irregular patterns plus unique patterns related to survey error. In other words, the CPS unemployment rate for any given month is not only a combination of trend, seasonal and irregular influences, but also contains some influence from the survey error. The models for Oregon's CPS data describe the data as a combination of trend, seasonal and irregular patterns, or "signal," and survey noise. Thus, the models take the form of "signal-plus-noise."
The employment and unemployment models of the signal are bivariate models that relate information in their respective input variables to CPS employment and unemployment levels. The model input data - UI claims and total nonfarm payroll employment - are modeled along with their interaction with CPS unemployment and employment levels. A filtering algorithm, called the "forward filter," is used to compute the current monthly employment and unemployment estimates for Oregon. Basically, the filter combines CPS data with model-produced estimates to mitigate the influence of survey error on labor force levels.
Time series models create better estimates of the true labor market situation than the volatile survey of households can provide. Graph 2 shows how the model improves the estimate of the CPS unemployment rate found on Graph 1. The model smoothes out the large, volatile swings in the CPS monthly data by reducing the influence of survey error on the unemployment rate. The use of time series models provides a cost-effective way to increase the reliability of CPS data.
Economic time series data often exhibit seasonal fluctuations, movements in the data that recur during a specific time period each year. For example, the winter months in Oregon exhibit higher periods of unemployment while the summer months exhibit lower periods of unemployment. These higher and lower periods of unemployment are a product of the weather (or season), and obscure the underlying direction of unemployment.
The time series models behind Oregon's employment and unemployment data are designed to produce seasonally adjusted data. The models of the signal are written as a combination of trend, seasonal, and irregular components. Subtracting the seasonal component provides Oregon's seasonally adjusted unemployment rate. Graph 3 displays seasonally adjusted and unadjusted unemployment rates for Oregon. The seasonally adjusted unemployment rate cuts through the center of the unadjusted series, thereby revealing the underlying trend in unemployment that is obscured by the recurring seasonal peaks and troughs.
The real-time benchmark ensures that not seasonally adjusted employment and unemployment data for the 50 states and the District of Columbia sum to national labor force levels. This is accomplished in two phases. In the first phase, model-based employment and unemployment estimates for the nine census divisions are produced and benchmarked to national employment and unemployment data. In the second phase, model-based employment and unemployment estimates for each state are produced and benchmarked to their respective census division totals.
Oregon's model-produced data values are benchmarked to Pacific Census Division levels. This division includes Oregon, Washington, California, Alaska, and Hawaii.
Standard errors indicate the probable accuracy of Oregon's labor force estimates. They can be used to construct confidence intervals, or error ranges. An error range is a range of numbers above and below the estimate that likely contains the true value for a given level of confidence. The width of an error range indicates the level of uncertainty associated with an estimate. Wider error ranges contain more possible numbers that the true value is likely to assume. Thus, broad error ranges indicate less certainty about the accuracy of the estimate, while narrow error ranges indicate more accuracy. Oregon's preliminary June 2009 seasonally adjusted unemployment rate of 12.2 percent had an error range of 11.2 to 13.2 percent at the 90 percent confidence level.
Annual processing occurs shortly after the completion of each calendar year. At this time, more revisions are made to the labor force data. The historical series is reprocessed using a smoothing algorithm. Basically, this levels the data series by using more CPS time series data than the filter had to produce the preliminary numbers. Further, updates to the model inputs are incorporated into the labor force data at annual processing time.