The Occupational Employment Statistics (OES) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OES estimates are constructed from a sample of about 52,000 establishments. Each year, forms are mailed to two semiannual panels of approximately 8,500 sampled establishments, one panel in May and the other in November. Estimates are based on responses from six semiannual panels collected over a three-year period.
The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor funds the survey and provides the procedures and technical support. The New York State Department of Labor (NYSDOL) collects and processes the data.
Many of the concepts and definitions used in the OES survey are similar to those in the Current Employment Statistics survey, a monthly BLS payroll survey of nonagricultural establishments. Many others, however, are unique to the OES survey. Key definitions for the OES survey follow.
An establishment is an economic unit that produces goods or services, such as a factory, a mine, or a store. It is generally at a single location and predominantly engaged in one economic activity.
The OES survey defines employment as the number of workers who can be classified as full-time or part-time employees, including workers on paid vacations or other types of leave; workers on unpaid short-term absences; salaried officers, executives, and staff members of incorporated firms; employees temporarily assigned to other units; and employees for whom the reporting unit is their permanent duty station, regardless of whether that unit prepares their paycheck. The survey excludes the self-employed, owners/partners of unincorporated firms, and unpaid family workers. Employees are reported in their present occupation which might be different from the occupation for which they were trained.
Benchmark weights are used to compute estimates of occupational employment. Estimates are produced for cells defined by geographic area, industry group, and size of establishment (i.e., size class). Total employment for an occupation in a cell is estimated by taking the product of reported occupational employment and benchmark weight for each establishment in the cell and summing the product across all establishments in the cell
Wages for the OES survey are straight-time, gross pay, exclusive of premium pay. Base rate, cost-of-living allowances, guaranteed pay, hazardous-duty pay, incentive pay (including commissions and production bonuses, tips, and on-call pay) are included. Back pay, jury duty pay, overtime pay, severance pay, shift differentials, non-production bonuses, and tuition reimbursements are excluded.
The OES survey collects wage data for 12 wage intervals. That is, for each occupation, employers report the number of employees that fall within each wage range. The wage intervals used for the survey begin with hourly wages under $9.25 and annual wages under $19,240. The top category covers hourly wages of $90 and above, and annual salaries of $187,200 and above.
Hourly versus annual wage reporting: For each occupation, respondents are asked to report the number of employees whose wages fall within specific wage intervals. The intervals are defined both as hourly rates and as the corresponding annual rates. Annual rates are constructed by multiplying the hourly wage rate for the interval by the typical work year of 2,080 hours. In reporting, the respondent can reference either the hourly or the annual rate for full-time workers, but the respondent is instructed to report the hourly rate for part-time workers.
There are workers in some occupations whose pay is based on an annual amount and who generally work less than the usual 2,080 hours per year. Since the survey does not collect data on the actual number of hours worked, hourly rates cannot be calculated with a reasonable degree of confidence from the annual wages paid to these workers. For this reason, the annual salary is reported for these occupations. Occupations that typically have a work-year of less than 2,080 hours include certain musicians and entertainers, pilots, flight attendants, and teachers. In cases where an annual wage is not available, the entry will read N/A.
A mean wage and a median wage are calculated using wage data from establishments in the industries that reported employment for an occupation.
The mean wage is the estimated total wages for an occupation, divided by its weighted survey employment. A mean wage value is calculated for each wage interval based on occupational wage data collected by the Office of Compensation and Working Conditions of the U.S. Department of Labor. These interval mean wage values are then attributed to all workers reported in the interval. For each occupation, total weighted wages in each interval (i.e., mean wages times weighted employment) are summed across all intervals and divided by the occupation's weighted survey employment to obtain a mean wage.
The median wage is the estimated 50th percentile of the distribution of wages: 50 percent of workers in an occupation earn wages below the median wage, and 50 percent earn wages above. The wage interval containing the median wage is located using a cumulative frequency count of employment across wage intervals. After the targeted wage interval is identified, the median wage rate is estimated by a linear interpolation procedure.
The entry wage is the mean (average) of the bottom third of wages in an occupation.
The experienced wage is the mean (average) of the top two-thirds of wages in an occupation.
Scope of the Survey
In 2002, the OES survey switched from using the Standard Industrial Classification (SIC) System to using the North American Classification System (NAICS) to classify to which industry an establishment belongs. The scope of the survey includes establishments in NAICS sectors 11 (logging and agricultural support activities only), 21, 22, 23, 31-33, 42, 44-45, 48-49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81 (except private households), federal, state, and local government. Data for the U.S. Postal Service as well as the federal government are universe counts obtained from the Postal Service and the U.S. Office of Personnel Management, respectively.
New York State's Unemployment Insurance (UI) files provide the universe from which the OES survey draws its sample. The employment benchmarks are obtained from reports submitted by employers to the UI program. In some non-manufacturing industries, supplemental sources are used for establishments that do not report to the UI program. Samples selected in panels prior to May 2005 were stratified using MSA definitions based on the 1990 Metropolitan Statistical Area Standards. Beginning with the May 2005 panel, the sample was stratified using new MSA definitions based on the 2000 Metropolitan Statistical Area Standards. The OES survey sample is stratified by area, industry, and size class.
Method of Collection
Survey schedules initially were mailed to virtually all sampled establishments. Additional mailings were sent to non-responding establishments. Telephone follow-ups were made to non-responding establishments throughout the course of the survey.
New Occupational Classification Standards for 2010
The OES survey categorizes workers into nearly 800 detailed occupations based on the Office of Management and Budget's Standard Occupational Classification (SOC) system; together, these detailed occupations make up 22 of the 23 SOC major occupational groups. Major group 55, Military Specific Occupations, is not included. The 2010 OES estimates mark the first set of estimates based in part on data collected using the 2010 SOC system. Previous estimates were based on the 2000 SOC.
Almost all the occupations in this release are 2010 SOC occupations; however, some are not. In these cases, an estimate for a temporary occupation was created from data reported for one or more occupations in the 2000 SOC combined with data reported for one or more 2010 SOC occupations. Some occupations have the same title as a 2010 SOC occupation, but not the same content. These occupations are marked with an asterisk (*) and given a temporary code for the OES data. The May 2012 OES data will reflect the full set of detailed occupations in the 2010 SOC. For a list of all occupations, including 2010 SOC occupations, and how data collected on two structures were combined, see the OES Frequently Asked Questions online athttp://www.bls.gov/oes/oes_ques.htm#Ques41. For more information about the SOC system, please see the Bureau of Labor Statistics web site at http://www.bls.gov/soc/.
The OES survey is designed to produce estimates by combining six panels of data collected over a 3-year period. The 3-year period has approximately 52,000 sample members, and approximately 8,500 establishments per panel. Each semiannual panel represents a one-sixth sample of the full three-year sample plan. While estimates can be made from a single year of data, the OES survey has been designed to produce estimates using the fullthree years of data. The full three-year sample allows the production of estimates at fine levels of geography, industry, and occupational detail, while estimates using any one year of data would be subject to a higher sampling error (due to the smaller sample size) and the limitations associated with having only one-third of the units from the certainty strata.
Producing estimates withthree years of sample data reduces sampling error significantly, particularly for small geographic areas and occupations. However, this process also has some quality limitations in that it requires the adjustment of earlier years' data to the current reference period, a procedure called "wage updating."
Starting with the 1997 estimates, the OES program has used the over-the-year fourth-quarter wage changes from the Bureau's Employment Cost Index (ECI) to adjust prior-year survey data before combining it with the data for the current year. The wage updating procedure assumes that each occupation's wage, as measured in the earlier years, moves according to the average movement of its occupational division, and that there are no major geographic or detailed occupational differences. This may not be the case. BLS has conducted research over the past several years on the accuracy of the ECI wage-updating method versus other modeling approaches. Current research results support the continued use of the ECI wage-updating methodology.
The 2002 and later estimates also use the estimation methodology introduced in 1997, which uses a "nearest neighbor" imputation approach for non-respondents and applies employment benchmarks at a detailed MSA by three-digit industry and broad size-class level.
The New York State Department of Labor used wage-updating factors for later time periods to further update the data to a more current time period, the first quarter of 2012. As a result, wage-updating factors have been applied to all of the data included in these estimates. The updated data contained in this report are not official BLS data series and BLS has not validated them. These wage estimates reflect New York State s minimum wage of $7.25. The New York State Department of Labor may adjust estimates to reflect changing economic conditions, updates, or changes in State or Federal law.
Reliability of the Estimates
The occupational employment and wage rates in this report are estimates derived from a sample survey. Two types of errors are possible in an estimate based on a sample survey -- sampling error and nonsampling error. Sampling error occurs because the observations are based on a sample, rather than on the entire population. Nonsampling error is due to response, nonresponse, and operational errors.
Nonsampling errors: Estimates are subject to various response, nonresponse, and operational errors during the survey process. Sources of possible errors are data collection, response, coding, transcription, data editing, nonresponse adjustment, and estimation. These errors would also occur if a complete census were to be conducted under the same conditions as the sample survey. Explicit measures of the effects of these errors are not available. However, it is believed that the important response and operational errors were detected and corrected during the review and validation process.
Another potential bias derives from the nature of the industry-specific survey forms. To reduce the paperwork burden of responders, survey forms do not list all of the nearly 800 SOC occupations. Rather, the forms focus on the hundred or so occupations most likely to be found in that industry. Although responders are provided space on the back of each form to write in missing occupational titles, we cannot guarantee they adhere to this request. Should a significant portion of responders fail to report or misclassify workers in unlisted titles, the employment and wage data could be affected. The extent of this bias is unknown.
The limitations placed on the size of the benchmark factors are another source of potential bias. A benchmark factor is the ratio of a known employment value to a sample-derived employment estimate. This factor is used to make a post-stratification adjustment, forcing the calculated total weighted employment estimate [at the state-Metropolitan Statistical Area (MSA) / 4-digit NAICS (with 5-digit exceptions) employment-size class level] to match the population employment (at that same level). The source of the population employment data is New York's Quarterly Unemployment Insurance files for the reference period of the survey.
In cases where a small sample was taken, the ratio factor can become large or small. In order to prevent an establishment from contributing either too much or not enough to an MSA's occupational employment and wage rate estimates, the benchmark factor was not allowed to exceed a predetermined value. The total employment count for the MSAs where the benchmark factor was limited by this ceiling will be biased to a small degree in those strata. The employment not assigned to those strata because of this ceiling was then distributed across the other MSAs in the state/4-digit industry, so that the estimated employment of the state/4-digit industry would match the known employment totals at that level.
Sampling errors: The particular sample used in this survey is one of a large number of possible samples of the same size that could have been selected using the same sample design. For example, occupational employment and wage rate estimates derived from the different samples will differ from one another. The deviation of a sample estimate from the average of all possible sample estimates is called the sampling error. The standard error of an estimate is a measure of the variation of estimates across all possible samples and thus is a measure of the precision with which an estimate from a particular sample approximates the average result of all possible samples.
Quality Control Measures
Quality control measures implemented in the OES survey include the following:
- follow-up solicitations of non-respondents (especially critical non--respondents)
- review of survey schedules to verify the accuracy and reasonableness of the reported data
- adjustments of atypical reporting units on the data file
- validation of the non-response adjustment factors
- validation of the population employment and ratio factors
- standardized data processing programs and activities