Technical Note This news release presents statistics from two major surveys, the Current Population Survey (CPS; household survey) and the Current Employment Statistics survey (CES; establishment survey). The household survey provides information on the labor force, employment, and unemployment that appears in the "A" tables, marked HOUSEHOLD DATA. It is a sample survey of about 60,000 eligible households conducted by the U.S. Census Bureau for the U.S. Bureau of Labor Statistics (BLS). The establishment survey provides information on employment, hours, and earnings of employees on nonfarm payrolls; the data appear in the "B" tables, marked ESTABLISHMENT DATA. BLS collects these data each month from the payroll records of a sample of nonagricultural business establishments. Each month the CES program surveys about 141,000 businesses and government agencies, representing approximately 486,000 individual worksites, in order to provide detailed industry data on employment, hours, and earnings of workers on nonfarm payrolls. The active sample includes approximately one-third of all nonfarm payroll employees. For both surveys, the data for a given month relate to a particular week or pay period. In the household survey, the reference period is generally the calendar week that contains the 12th day of the month. In the establishment survey, the reference period is the pay period including the 12th, which may or may not correspond directly to the calendar week. Coverage, definitions, and differences between surveys Household survey. The sample is selected to reflect the entire civilian noninstitutional population. Based on responses to a series of questions on work and job search activities, each person 16 years and over in a sample household is classified as employed, unemployed, or not in the labor force. People are classified as employed if they did any work at all as paid employees during the reference week; worked in their own business, profession, or on their own farm; or worked without pay at least 15 hours in a family business or farm. People are also counted as employed if they were temporarily absent from their jobs because of illness, bad weather, vacation, labor-management disputes, or personal reasons. People are classified as unemployed if they meet all of the following criteria: they had no employment during the reference week; they were available for work at that time; and they made specific efforts to find employment sometime during the 4-week period ending with the reference week. Persons laid off from a job and expecting recall need not be looking for work to be counted as unemployed. The unemployment data derived from the household survey in no way depend upon the eligibility for or receipt of unemployment insurance benefits. The civilian labor force is the sum of employed and unemployed persons. Those not classified as employed or unemployed are not in the labor force. The unemployment rate is the number unemployed as a percent of the labor force. The labor force participation rate is the labor force as a percent of the population, and the employment-population ratio is the employed as a percent of the population. Additional information about the household survey can be found at www.bls.gov/cps/documentation.htm. Establishment survey. The sample establishments are drawn from private nonfarm businesses such as factories, offices, and stores, as well as from federal, state, and local government entities. Employees on nonfarm payrolls are those who received pay for any part of the reference pay period, including persons on paid leave. Persons are counted in each job they hold. Hours and earnings data are produced for the private sector for all employees and for production and nonsupervisory employees. Production and nonsupervisory employees are defined as production and related employees in manufacturing and mining and logging, construction workers in construction, and nonsupervisory employees in private service- providing industries. Industries are classified on the basis of an establishment’s principal activity in accordance with the 2012 version of the North American Industry Classification System. Additional information about the establishment survey can be found at www.bls.gov/ces/#technical. Differences in employment estimates. The numerous conceptual and methodological differences between the household and establishment surveys result in important distinctions in the employment estimates derived from the surveys. Among these are: --The household survey includes agricultural workers, the self- employed, unpaid family workers, and private household workers among the employed. These groups are excluded from the establishment survey. --The household survey includes people on unpaid leave among the employed. The establishment survey does not. --The household survey is limited to workers 16 years of age and older. The establishment survey is not limited by age. --The household survey has no duplication of individuals, because individuals are counted only once, even if they hold more than one job. In the establishment survey, employees working at more than one job and thus appearing on more than one payroll are counted separately for each appearance. Seasonal adjustment Over the course of a year, the size of the nation's labor force and the levels of employment and unemployment undergo regularly occurring fluctuations. These events may result from seasonal changes in weather, major holidays, and the opening and closing of schools. The effect of such seasonal variation can be very large. Because these seasonal events follow a more or less regular pattern each year, their influence on the level of a series can be tempered by adjusting for regular seasonal variation. These adjustments make nonseasonal developments, such as declines in employment or increases in the participation of women in the labor force, easier to spot. For example, in the household survey, the large number of youth entering the labor force each June is likely to obscure any other changes that have taken place relative to May, making it difficult to determine if the level of economic activity has risen or declined. Similarly, in the establishment survey, payroll employment in education declines by about 20 percent at the end of the spring term and later rises with the start of the fall term, obscuring the underlying employment trends in the industry. Because seasonal employment changes at the end and beginning of the school year can be estimated, the statistics can be adjusted to make underlying employment patterns more discernable. The seasonally adjusted figures provide a more useful tool with which to analyze changes in month-to-month economic activity. Many seasonally adjusted series are independently adjusted in both the household and establishment surveys. However, the adjusted series for many major estimates, such as total payroll employment, employment in most major sectors, total employment, and unemployment are computed by aggregating independently adjusted component series. For example, total unemployment is derived by summing the adjusted series for four major age-sex components; this differs from the unemployment estimate that would be obtained by directly adjusting the total or by combining the duration, reasons, or more detailed age categories. For both the household and establishment surveys, a concurrent seasonal adjustment methodology is used in which new seasonal factors are calculated each month using all relevant data, up to and including the data for the current month. In the household survey, new seasonal factors are used to adjust only the current month's data. In the establishment survey, however, new seasonal factors are used each month to adjust the three most recent monthly estimates. The prior 2 months are routinely revised to incorporate additional sample reports and recalculated seasonal adjustment factors. In both surveys, 5-year revisions to historical data are made once a year. Reliability of the estimates Statistics based on the household and establishment surveys are subject to both sampling and nonsampling error. When a sample rather than the entire population is surveyed, there is a chance that the sample estimates may differ from the "true" population values they represent. The exact difference, or sampling error, varies depending on the particular sample selected, and this variability is measured by the standard error of the estimate. There is about a 90-percent chance, or level of confidence, that an estimate based on a sample will differ by no more than 1.6 standard errors from the "true" population value because of sampling error. BLS analyses are generally conducted at the 90-percent level of confidence. For example, the confidence interval for the monthly change in total nonfarm employment from the establishment survey is on the order of plus or minus 100,000. Suppose the estimate of nonfarm employment increases by 50,000 from one month to the next. The 90-percent confidence interval on the monthly change would range from -50,000 to +150,000 (50,000 +/- 100,000). These figures do not mean that the sample results are off by these magnitudes, but rather that there is about a 90-percent chance that the "true" over-the-month change lies within this interval. Since this range includes values of less than zero, we could not say with confidence that nonfarm employment had, in fact, increased that month. If, however, the reported nonfarm employment rise was 250,000, then all of the values within the 90-percent confidence interval would be greater than zero. In this case, it is likely (at least a 90-percent chance) that nonfarm employment had, in fact, risen that month. At an unemployment rate of around 5.5 percent, the 90-percent confidence interval for the monthly change in unemployment as measured by the household survey is about +/- 280,000, and for the monthly change in the unemployment rate it is about +/-0.19 percentage point. In general, estimates involving many individuals or establishments have lower standard errors (relative to the size of the estimate) than estimates which are based on a small number of observations. The precision of estimates also is improved when the data are cumulated over time, such as for quarterly and annual averages. The household and establishment surveys are also affected by nonsampling error, which can occur for many reasons, including the failure to sample a segment of the population, inability to obtain information for all respondents in the sample, inability or unwillingness of respondents to provide correct information on a timely basis, mistakes made by respondents, and errors made in the collection or processing of the data. For example, in the establishment survey, estimates for the most recent 2 months are based on incomplete returns; for this reason, these estimates are labeled preliminary in the tables. It is only after two successive revisions to a monthly estimate, when nearly all sample reports have been received, that the estimate is considered final. Another major source of nonsampling error in the establishment survey is the inability to capture, on a timely basis, employment generated by new firms. To correct for this systematic underestimation of employment growth, an estimation procedure with two components is used to account for business births. The first component excludes employment losses from business deaths from sample-based estimation in order to offset the missing employment gains from business births. This is incorporated into the sample-based estimation procedure by simply not reflecting sample units going out of business, but imputing to them the same employment trend as the other firms in the sample. This procedure accounts for most of the net birth/death employment. The second component is an ARIMA time series model designed to estimate the residual net birth/death employment not accounted for by the imputation. The historical time series used to create and test the ARIMA model was derived from the unemployment insurance universe micro-level database, and reflects the actual residual net of births and deaths over the past 5 years. The sample-based estimates from the establishment survey are adjusted once a year (on a lagged basis) to universe counts of payroll employment obtained from administrative records of the unemployment insurance program. The difference between the March sample-based employment estimates and the March universe counts is known as a benchmark revision, and serves as a rough proxy for total survey error. The new benchmarks also incorporate changes in the classification of industries. Over the past decade, absolute benchmark revisions for total nonfarm employment have averaged 0.3 percent, with a range from -0.7 to 0.6 percent. Other information Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; Federal Relay Service: (800) 877-8339.