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The Occupational Employment Statistics (OES) survey of the U.S. Bureau of Labor Statistics (BLS) is a large survey of employers in the United States. The data from this survey are used to produce estimates of employment and wages by occupation at industry, state, and metropolitan area levels. They are used by the Office of Foreign Labor Certification of the U.S. Department of Labor in setting pay rates for workers in the United States on certain work visas. They are also key inputs to the Employment Projections program, which is one of the most widely used products at BLS. However, the OES data are little known by most researchers.
While the published estimates of the OES program are not designed for making comparisons over time, the confidential microdata underlying these estimates do allow for following certain employers over time, and these data have several features that make them particularly useful for research purposes. Most notably, the microdata are a very large set of data containing full staffing patterns, including occupation and wage information for all employees within surveyed establishments.
This article describes the longitudinal features of the OES data and the composition of the establishments that are observed longitudinally. We begin with a discussion of changes in the OES survey that affect the probability of sample selection over time for any particular establishment. We then show how the sample design shapes the composition of establishments sampled repeatedly for the OES program. Next, we show how differences in response rates also shape the composition of establishments observed repeatedly. Finally, we compare the distributions of employer and employee characteristics in the longitudinal OES data with the distribution of these characteristics in the cross-sectional data.
BLS began conducting surveys of employment by occupation in the 1960s.1 It was in 1971 that the Occupational Employment Statistics survey began, and it was a collaboration between BLS and 10 State Employment Security agencies. The OES initially was a survey of 50,000 manufacturing establishments, asking these establishments about the distribution of their employees by occupation. Similar surveys were soon developed for other industries. Beginning in 1977, surveys were conducted in every state and the District of Columbia. Since then, major changes to the design of the OES survey have included the following:3 The OES sample design is a complex stratified random sample, which differs in several ways from a simple random sample of employers. The goal of the sample design is to produce estimates of employment and wages for each occupation with as little sampling variability as possible given the overall sample size. Portions of the sample are allocated to each industry, each geographic region, and (before 2005) each employer size class to reduce the variation of estimates of employment and wages by occupation for detailed industries and detailed geographic areas. Since 2006, the occupational variability of industries has also been a factor in the sample design—to increase the precision of the estimates, the sample size is larger in industries with more variation in the composition of occupations, and these industries are more likely to be selected for the OES sample. In addition, the sample design considers the size of employers as a criterion for selection. Large employers, which affect overall average employment and wage levels in their industry or geographic area, are always selected for the OES sample during the six-panel cycle. Smaller employers are selected with a probability that varies by the number of employers in their stratum (industry and geographic area) and by the number of employees in their establishment.5 Current methodology selects small- and medium-sized employers within each stratum with a probability proportionate to their employment levels. However, even very small employers are almost certain to be selected for the OES sample in each six-panel cycle if there are few other employers in their industry and geographic area. When there are fewer than four establishments in a stratum, all are selected for the OES sample. When there are 4–12 establishments (4–18 establishments in Idaho, Montana, or Wyoming until 2009) in a stratum, at least 3 are selected for the OES sample. When there are additional establishments in a stratum, at least six are selected for the sample.6 To reduce the burden of survey response for smaller employers, BLS assigns each known establishment a permanent random number (PRN) between 0 and 1. Within each stratum, establishments are selected for the OES sample beginning with those having a PRN value of 0.4, and establishments with PRN values greater than 0.4 have an increased probability of sample selection in every wave of the OES sample. Other BLS surveys begin selecting their random samples at different PRN values, making it unlikely that small establishments will be selected for multiple BLS surveys.7 The large number of employers in the OES sample and the stratified design of the sample mean that hundreds of thousands of employers have been included in the OES sample multiple times, albeit usually at least 3 years apart. This feature allows researchers with access to the OES microdata the opportunity to study changes in the occupational employment and wage patterns of the repeatedly sampled employers. As described above, the sample design of the OES survey leads to an increased probability of selection for large employers, employers in geographic areas with relatively few employers, employers in industries with a greater variability of occupational employment, and employers with a BLS internally assigned PRNr greater than 0.4. Such employers are particularly likely to be sampled repeatedly in the OES survey. Repeated sampling magnifies differences in sampling probability that occur in each wave of a survey. Consider a much simpler example than the complex OES survey: large employers have a 50-percent chance of being sampled, and small employers have a 10-percent chance of being sampled. To make population estimates for a single wave of data collection of this simple survey, weights of 2 should be applied for large employers and weights of 10 should be applied to small employers. However, if there are two independent samples drawn for two waves of this survey, the chance that a large employer will appear in both waves is .5 × .5, or 25 percent, while the chance that a small employer will appear in both waves is .10 × .10, or 1 percent. In this example, the weights used for a single wave of data collection will be inappropriate to use in weighting the data observed twice to population levels. Furthermore, the sample of smaller employers observed twice may be too small to make conclusions about this subpopulation. We examine how differences in the OES cross-sectional sampling probabilities across establishment sizes, geographic area types, and industries translate into differences in the probability that establishments with different characteristics appear in longitudinal OES data. For our analyses, we begin with private sector data from 1999, as these data are comparable to later data in their industry and occupational coding systems. (We drop 4,238 establishments from our analyses because they were not used for the 2002 estimates and so their industries were not recoded using the NAICS.) We omit the large number of public sector establishments included in the OES survey (such as publicly owned schools and hospitals), as well as establishments sampled in Guam, Puerto Rico, and the Virgin Islands. The frame for this sample is the Quarterly Census of Employment and Wages (QCEW), assembled from the reports to the unemployment insurance system made by nonfarm establishments in every state, and supplemented with a list of rail transportation employers (not covered by the unemployment insurance system).8 Table 1 shows the number of private sector establishments selected for sampling by the OES program from fall 1999 through May 2014, by establishment size. In all, more than 2.8 million establishments have been selected for the OES sample, of which nearly 1.3 million (46 percent) have been sampled more than once and nearly 80,000 have been sampled five or more times during this period.9 This table uses the employer sizes reported in the QCEW, as these are the estimates of employer size used to select the sample. Establishments sampled more than once are classified in this table by their largest size class. As shown in table 1, large establishments are more likely than small establishments to be sampled repeatedly by the OES program. Only 23 percent of the establishments with fewer than five employees that are ever sampled for the OES survey are sampled more than once (7 percent are sampled at least three times). However, 80 percent of the establishments with 250 or more employees that are ever sampled for the OES survey are sampled more than once, and 61 percent of these establishments are sampled at least three times. Table 2 shows the number of establishments selected for sampling by the OES program during this same period by area type. (Some area definitions change over time, and so establishments sampled more than once are classified in this table by the type of area in which they are located the final time they are sampled.) Approximately 46 percent of establishments ever sampled by the OES program in urban (MSA) areas and 45 percent ever sampled in rural (BOS) areas are selected for sampling more than once. Within urban (MSA) areas, establishments located in areas with less employment are more likely to be repeatedly sampled. About 48 percent of establishments ever sampled by the OES program in MSAs with employment of less than 100,000 are sampled more than once, while 45 percent of establishments ever sampled by the OES program in MSAs with employment of more than 1,000,000 are sampled more than once. Less than 100,000 100,000–249,999 250,000–499,999 Less than 100,000 100,000–249,999 250,000-499,999 500,000–999,999 1,000,000 or more Table 3 shows the number of establishments selected for the OES sample during this period, by industry. (Establishments may change industry classifications over time, and so establishments sampled more than once are classified in this table by the industry they report to the QCEW the final time they are sampled.) The frequency with which establishments are sampled more than once by the OES program varies by industry. The industry with the least amount of repeated sampling is accommodation and food services (NAICS code 72), in which 38 percent of establishments sampled are sampled more than once. This relatively low percentage is due to a combination of little variation by occupation between establishments, a large number of establishments, a large number of small establishments, and a large number of establishment openings and closures. The industries with the greatest amount of repeated sampling are manufacturing (NAICS code 31–33) and utilities, in which, respectively, 56 percent and 57 percent of establishments sampled are sampled more than once. Establishments in these industries are sampled more frequently because of their large size. Total OES establishments Notes: (1) These are two-digit industries according to the National American Industry Classification System (NAICS). NAICS codes are shown in parentheses. Source: U.S. Bureau of Labor Statistics. Figure 1 shows the number of establishments sampled by the OES program and those sampled more than once, by the panels in which they are sampled. This figure shows that establishments sampled more than once for the OES program come from all waves of data collection. It also shows the clear drop in sample size per panel in 2002, when the sample collection was split from one panel each fall to two panels, in May and November, of each year. Otherwise, the distribution of the potential sample for longitudinal observations of the OES data is remarkably similar from panel to panel, aside from an unusually small overall sample size in May 2008 because of budget constraints10 and an unusually small number of the establishments sampled multiple times that were sampled in November 2006.11 By far the most common interval between sample dates is exactly 3 years. Intervals of exactly 3 years represent 47 percent of all intervals between successive sample dates. Intervals of whole years are more common than intervals, such as 3 ½ years, that are off by half a year (because all establishments of very large companies are sampled at regular 3-year intervals), but the frequency with which each interval is observed is otherwise decreasing in interval length—that is, there are more intervals of 3 years than of 4 years, more intervals of 4 years than of 5 years, and so forth. The greater the number of times an establishment is sampled, the shorter the average interval between sample dates. Less than 1 percent of intervals between sample dates are spaced less than 3 years apart. About 3 percent of intervals between sample dates are 10 years or longer. The composition of longitudinal data from the OES survey is affected not only by the sample design but also by differential response rates to the OES survey. The OES survey is primarily conducted by mail, in a collaboration between BLS and State Workforce Agencies. There is an open-ended form to collect data from small employers, and, until November 2015, there were 97 different industry-specific forms used for collecting occupational employment and wage data from medium- and large-sized employers. After the initial mailing of forms, three industry-specific followup mailings are sent to nonrespondents at intervals of 3–4 weeks. The State Workforce Agencies also conduct followup by telephone, with timing varying by state.12 These agencies collect some data for large establishments via personal visits. Large numbers of respondents choose to respond to the survey in electronic format. Establishments that are sampled more than once may respond to the survey in some panels but not in others. Table 4 shows the number of times that establishments are sampled, by the number of times they respond. There were 2,840,923 establishments sampled for the OES survey between fall 1999 and May 2014, and over this period, these establishments were sampled 5,283,824 times. The probability of always responding declines as the number of times sampled increases: 78 percent of establishments sampled only once responded to the survey (some went out of business in the year between the sample frame date and the survey date), while 65 percent of those sampled twice responded twice, 57 percent of those sampled three times responded three times, 50 percent of those sampled four times responded four times, and 38 percent of those sampled five times responded five times. However, enough of those sampled responded each time that 76 percent of establishments responding exactly twice were sampled only twice, 76 percent of those responding exactly three times were sampled only three times, and 85 percent of those responding exactly four times were sampled only four times. 1 2 3 4 5 or more Total Source: U.S. Bureau of Labor Statistics. Figure 2 shows response rates for private sector establishments sampled by the OES program were steady at about 77 percent from 1999 through 2010 or 2011. There was a noticeable decline in response rates in 2013 because of the federal government shutdown in October 2013.13 State Workforce Agencies are required to follow up with employers (except when funding for this work lapses in a federal government shutdown) until the overall response rate for the survey reaches at least 75 percent overall (including the establishments of state and local governments) and 75 percent in each MSA. Data are collected from state governments only in the November panels, and so state analysts do not need to follow up with as many private sector establishments to reach these response targets in the November panels as in other months. Response to the OES survey has been mandatory in North Carolina since 1995, Oklahoma since 2000, South Carolina during the 2002–05 period, Georgia since 2005, Vermont since November 2012, Wyoming since May 2013, the District of Columbia since November 2014, and Hawaii and Oregon since May 2015. Private sector response rates are about 85 percent in these states when response is mandatory.14 Table 5 shows that small establishments generally have higher response rates than large establishments. Polly A. Phipps and Carrie K. Jones report that this is due to the preference of state analysts for focusing followup work on the smallest establishments.15 When analysts contact employers by phone, it is easiest both to find someone who can report on the occupational employment and wage information and to code occupational data over the phone for the smallest establishments. Hence, in working to reach the required 75-percent response targets across all establishments, state analysts find it easiest to focus on these small establishments. Table 6 shows that rural areas (BOS areas) have higher response rates than urban areas (MSAs), and among urban areas, response rates are higher in smaller MSAs than in larger MSAs. Phipps and Jones report that establishments in larger MSAs are both less likely to respond by mail to the survey forms and more difficult for state analysts to reach by telephone; these establishments often state that they are too busy to respond to the OES survey.16 Less than 100,000 100,000–249,999 250,000–499,999 Less than 100,000 250,000–499,999 500,000–999,999 1,000,000 or more Table 7 shows response rates by industry. Response rates are particularly high in establishments within the agriculture, forestry, fishing, and hunting sector (farms are outside the scope of the survey) and in some service industry establishments. Response rates are particularly low for company headquarters establishments; administrative and support and waste management and remediation establishments; mining, quarrying, and oil and gas extraction establishments; and information establishments. Notes: (1) These are two-digit industries according to the National American Industry Classification System (NAICS). NAICS codes are shown in parentheses. Source: U.S. Bureau of Labor Statistics. When sampled establishments do not respond to the OES survey, data for these establishments are imputed in order to produce estimates of occupational employment and wages by industry and geographic area. These imputations proceed in two steps. First, occupational staffing patterns are taken from establishments similar in industry, overall employment size (as reported to the unemployment insurance system), and geographic area. Second, wage distributions are taken from the distribution of wages for the same occupation among employers in the same geographic area, in the same industry, and with the same overall employment size.17 In assembling the longitudinal OES data described in the next section, we do not include this imputed data for establishments that do not respond to the OES survey. The combination of sampling and response patterns described above shape the composition of establishments that respond more than once to the OES survey, sometimes in opposing ways. Figure 3 shows the composition of the longitudinal data by panel and by the number of times establishments appear in the longitudinal data. Establishments are disproportionately likely to be observed in the fall 1999, fall 2000, and fall 2001 panels, which had larger sample sizes than more recent panels. Because of an error in the selection of the sample for the November 2006 panel, establishments in the longitudinal data are less likely to appear in that panel.18 Establishments appearing five times in the longitudinal data are less likely to appear in the November 2011 or November 2008 data. We believe this pattern will disappear once the data of the November 2014 panel are added to the longitudinal data, because so many establishments are sampled at regular 3-year intervals. The distribution of establishment and (weighted) employee characteristics in the longitudinal sample is remarkably similar to the distribution of each characteristic within the cross-section of OES data as a whole. OES employment estimates use establishment weights based on the inverse of the probability of sample selection. These estimates are then benchmarked to total employment levels by industry and geographic area.19 We use these same weights in tables 8 through 11.20 Overall, 973,857 of the 2.8 million private sector establishments ever sampled by the OES program, or 34 percent, appear in the longitudinal data. These 973,857 establishments are all observed multiple times in the longitudinal data, and establishment observations total 2,562,195. These 2,562,195 establishment observations in the longitudinal data represent 48 percent of the 5,283,824 private sector observations and imputed observations in the OES data. Summed over all panels from 1999 through May 2014, weighted employment in the longitudinal sample is 268 million, half the weighted private sector employment in the OES data. Estimation weights in the OES data are designed to represent the U.S. workforce at the end of each 3-year data collection period: reweighting to an average of these employment levels over the fall 1999–May 2014 period gives an employment level in the longitudinal data of 57 million workers out of a total private sector workforce of 113.8 million (not shown in the tables below). Table 8 shows the distribution by establishment size class, categorizing establishments in the longitudinal sample by their size when they last responded. The smallest establishments are the size class that appears least frequently in the longitudinal data relative to the number of times these establishments are observed in the OES data as a whole. Such establishments represent 12.6 percent of all establishments (16.5 percent of observations) in the longitudinal data and 21.5 percent of all establishments (18.7 percent of observations) in the OES data as a whole. Because the probability that small units will be sampled is low, they are assigned higher weights for calculating estimates; their weighted employment is 6.3 percent of the employment in the longitudinal data, which is remarkably close to their 6.7 percent of weighted employment in the OES data as a whole. After estimation weights are used to calculate employment totals, the most overrepresented size class in the longitudinal data is the class of establishments with 10–19 employees, which has weighted employment representing 12.7 percent of all employment in the longitudinal data and 11.9 percent of employment in the OES data as a whole. Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Table 9 shows a similar comparison by area type. Establishments and employment in rural areas are slightly overrepresented in the longitudinal data: rural establishments comprise 21.5 percent of establishments, 21.5 percent of establishment-observations, and 14.0 percent of weighted total employment in the longitudinal data, compared with 20.9 percent of establishments, 20.6 percent of establishment-observations, and 13.3 percent of weighted total employment in the private sector OES data as a whole. The area type most underrepresented in the longitudinal data is those with employment greater than 1 million: these establishments comprise 20.9 percent of total establishments and 29.9 percent of weighted total employment in the longitudinal data, compared with 22.7 percent of establishments and 31.8 percent of weighted total employment in the OES data as a whole. Less than 100,000 Percent of total 100,000–249,999 Percent of total 250,000–499,999 Percent of total Less than 100,000 Percent of total 100,000–249,999 Percent of total 250,000–499,999 Percent of total 500,000–999,999 Percent of total 1,000,000 or more Percent of total Table 10 gives a similar comparison by industry. Again, the similarity of distributions in the composition by industry of establishments, establishment observations, and weighted employment levels between the longitudinal sample and the OES data as a whole is remarkable. The industry most overrepresented in the distribution of establishments (and establishment observations) in the longitudinal data is manufacturing (NAICS code 31–33), which contains 12.5 percent of establishments (13.0 percent of establishment observations) in the longitudinal data and 10.0 percent of establishments (11.5 percent of establishment observations) in the OES data as a whole. This is not surprising, given the large establishment sizes in manufacturing described above. The industry most underrepresented in the distribution of establishments is accommodation and food services (NAICS code 72), which contains 4.6 percent of establishments in the longitudinal data and 5.9 percent of establishments in the OES data as a whole. This is not surprising, given the low variation in the occupational composition of establishments in this industry, reducing sampling probabilities. The industries in which the fraction of weighted employment in the longitudinal data differs most from the fraction of weighted employment in the OES data as a whole are health care and social assistance (NAICS code 62), which contains 15.9 percent of weighted employment in the longitudinal data and 14.0 percent of weighted employment in the OES data as a whole, and administrative and support and waste management and remediation (NAICS code 56), which contains 5.7 percent of weighted employment in the longitudinal data and 7.1 percent of weighted employment in the OES data as a whole. Administrative support and waste management and remediation is somewhat underrepresented in the weighted employment of the longitudinal data because of the low response rate among establishments in this industry, as described above. Health care and social assistance is somewhat overrepresented in the weighted employment of the longitudinal data because of a combination of a large industry size, a higher-than-average rate of repeated sampling and a slightly-higher-than-average response rate. Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Percent of total Notes: (1) These are two-digit industries according to the National American Industry Classification System (NAICS). NAICS codes are shown in parentheses. Source: U.S. Bureau of Labor Statistics. Table 11 makes a comparison (only for weighted employment) of longitudinal sample data and all OES data by the occupations of employees.21 Once again, the distributions of occupations are remarkably similar. The most underrepresented occupations in the longitudinal data is the management, business, and financial occupations group, which comprises 8.7 percent of weighted employment in the longitudinal data and 9.4 percent of weighted employment in the OES data as a whole. The most overrepresented occupations in the longitudinal data are the healthcare practice occupations, which comprise 6.1 percent of occupations in the longitudinal data and 5.4 percent of occupations in the OES data as a whole. Given the small overrepresentation of employment in the healthcare and social assistance industry in the longitudinal data, some overrepresentation of the occupational category associated with this industry is not surprising. Notes: (1) These are major occupation groups according to the Standard Occupational Classification (SOC) system. SOC codes are shown in parentheses. Source: U.S. Bureau of Labor Statistics. The OES program collects information on wages from each establishment surveyed. Employers are asked to report the number of employees in each occupation by wage range; employers select among 12 wage ranges. These wage ranges were constant from 1999 through May 2005 and were then adjusted in November 2005, November 2008, and November 2013.22 Figure 4 shows nominal wage distributions for private sector establishments in the longitudinal sample compared with wage distributions from all private sector establishments in the OES survey (including those with imputed wage data), in groups of panels with common wage ranges. For example, wage data for all panels from fall 1999 through May 2005 are combined in the upper left wage density plot, including multiple observations from establishments in the longitudinal sample if they responded more than once during this period. Overall, the wage distributions for private sector establishments in the longitudinal data are quite similar to wage distributions in the private sector of the OES data as a whole. However, the patterns of differences between these distributions vary over time. In the panels of 1999–May 2005, establishments in the longitudinal data had a larger percentage of workers earning $6.75–$16.99 per hour and a smaller percentage of workers earning less than $6.75 per hour than the OES data as a whole. Similarly, in the panels of November 2005–May 2008, establishments in the longitudinal data having a larger percentage of workers earning $9.50–$30.99 per hour and a smaller percentage of workers earning less than $9.50 per hour than the OES data as a whole. Overall, for the panels of fall 1999–May 2005 and November 2005–May 2008, there was less employment in the very bottom of the wage distribution and more employment in the middle of the wage distribution in the establishments of the longitudinal sample than in the establishments of the OES data as a whole. Later panels show more similarity between the longitudinal data and the OES data as a whole, and the patterns of differences change. For the panels of November 2008–May 2013 and November 2013–May 2014, there was more employment in the lower portion of the wage distribution and less employment in the upper portion of the wage distribution in the establishments of the longitudinal sample than in the establishments of the OES data as a whole. Although the OES program is designed to produce estimates of occupational employment and wages for specific industries and geographic areas, assembling the microdata collected by this program longitudinally permits the study of many additional topics. Zachary Warren used data from establishments that responded at least twice to the OES survey from 2000 to 2005 to examine changes in employment by occupation in growing and shrinking establishments.23 Dina Itkin used data from establishments that responded exactly twice to the OES survey from 2000 to 2006 to examine changes in employment and wages by occupation in establishments that changed ownership between their responses.24 Itkin and Laurie Salmon used data from establishments that appeared in the Mass Layoff Statistics Survey and responded at least twice to the OES survey from 1999 to 2008 to examine changes in employment and wages by occupation in establishments that were affected by mass layoffs.25 Those interested in using longitudinal microdata from the OES program should be aware that the establishments responding two or more times to the OES survey are not designed to be a representative sample of establishments or of employees in the United States. The OES sample is not a simple random sample but is designed to minimize the variance of estimates produced for the nation as a whole, for each industry, and for each geographic area. Therefore, larger establishments, establishments in certain industries, and establishments in geographic areas with fewer establishments are more likely to be sampled repeatedly. These differences in the probability of selection for different types of establishments will be compounded when researchers examine establishments that are selected for the survey more than once. However, the use of permanent random numbers in the OES sample design increases the likelihood that establishments will be repeatedly sampled for this survey. Furthermore, response to the OES survey is not random, and patterns of nonresponse act in opposite ways to the intent of the sample design. Smaller establishments and establishments in geographic areas with fewer establishments are more likely to respond to the survey. Overall, the characteristics of longitudinal microdata from the OES are remarkably representative of the OES data as a whole regardless of whether one examines establishment size, geographic area type, industry, occupation, or wage range. Thus, it is possible to use data collected from the OES to examine employment and wage patterns for establishments observed longitudinally—not only for the largest establishments that are most likely to be selected for this survey but for a broad range of establishment types. Visiting researcher program Longitudinal data from the Occupational Employer Statistics Survey are not publicly available. However, the BLS onsite Visiting Researcher program allows eligible researchers the opportunity to visit BLS as temporary special employees for the purpose of working with confidential data in approved statistical research projects.26 Under a grant from the National Science Foundation, BLS and the American Statistical Association offer research fellowships for this type of research by academic scholars.27 Funding opportunities for junior faculty and senior graduate students to engage in such research include the CSWEP Summer Economics Fellows Program.28Repeated sampling in the OES survey
Establishment size (number of employees) QCEW establishments Number of times sampled in the OES Total OES establishments Percentage of OES establishments repeated 1 2 3 4 5 or more Less than 5 10,228,755 468,995 100,627 31,920 9,022 1,006 611,570 23.3 5–9 3,536,326 324,004 120,095 52,938 18,822 2,328 518,187 37.5 10–19 2,544,162 298,622 137,637 78,377 38,690 7,663 560,989 46.8 20–49 1,744,980 255,205 138,405 91,686 60,039 17,465 562,800 54.7 50–99 595,170 118,501 72,546 50,952 36,775 12,711 291,485 59.3 100–249 333,982 58,996 49,356 39,800 33,733 15,900 197,785 70.2 250 or more 139,708 19,876 17,921 17,296 20,155 22,859 98,107 79.7 Total 19,123,083 1,544,199 636,587 362,969 217,236 79,932 2,840,923 45.6 Source: U.S. Bureau of Labor Statistics. Type of area and employment level QCEW establishments Number of times sampled in the OES Total OES establishments Percentage of OES establishments repeated 1 2 3 4 5 or more Rural 2,112,957 136,957 53,980 29,850 15,440 3,956 240,183 43.0 1,524,318 160,407 70,899 40,923 23,818 7,724 303,771 47.2 266,405 29,207 10,557 5,791 3,628 1,351 50,534 42.2 Urban 1,402,735 234,579 109,340 64,462 35,663 9,558 453,602 48.3 2,030,685 266,522 107,278 62,331 37,055 11,933 485,119 45.1 1,768,309 163,046 71,291 40,982 25,153 9,797 310,269 47.5 2,730,586 199,147 74,989 41,935 26,212 10,564 352,847 43.6 7,287,088 354,334 138,253 76,695 50,267 25,049 644,598 45.0 Total 19,123,083 1,544,199 636,587 362,969 217,236 79,932 2,840,923 45.6 Source: U.S. Bureau of Labor Statistics. Industry(1) QCEW establishments Number of times sampled in the OES Percentage of OES establishments repeated 1 2 3 4 5 or more Agriculture, forestry, fishing and hunting (11) 194,450 8,650 3,466 1,797 896 291 15,100 42.7 Mining, quarrying, and oil and gas extraction (21) 68,583 9,551 4,226 2,404 1,639 580 18,400 48.1 Utilities (22) 29,523 6,159 3,374 2,401 1,679 695 14,308 57.0 Construction (23) 1,870,464 133,353 50,783 27,993 16,210 5,550 233,889 43.0 Manufacturing (31–33) 706,382 125,711 65,553 45,833 32,498 13,820 283,415 55.6 Wholesale trade (42) 1,395,271 107,842 47,141 28,527 18,881 6,692 209,083 48.4 Retail trade (44–45) 2,204,123 237,797 91,760 47,008 25,330 6,758 408,653 41.8 Transportation and warehousing (48–49) 539,593 56,594 24,116 13,975 8,020 2,923 105,628 46.4 Information (51) 378,723 47,610 20,069 11,119 6,327 1,993 87,118 45.3 Finance and insurance (52) 1,038,620 89,597 33,906 18,080 9,748 3,245 154,576 42.0 Real estate and rental and leasing (53) 788,909 56,208 22,638 12,189 6,471 1,916 99,422 43.5 Professional, scientific, and technical services (54) 2,361,651 140,347 55,123 30,217 16,755 5,874 248,316 43.5 Management of companies and enterprises (55) 116,372 13,699 6,456 4,103 3,184 1,889 29,331 53.3 Administrative and support and waste management and remediation (56) 1,131,403 109,550 43,898 23,788 12,909 4,874 195,019 43.8 Educational services (61) 199,999 25,361 10,774 6,211 3,633 1,815 47,794 46.9 Health care and social assistance (62) 2,543,242 135,419 61,945 37,023 22,664 10,752 267,803 49.4 Arts, entertainment, and recreation (71) 272,803 34,788 14,362 8,384 5,566 2,479 65,579 47.0 Accommodation and food services (72) 1,377,978 103,306 33,345 16,487 9,898 3,175 166,211 37.8 Other services (81) 1,904,994 102,657 43,652 25,430 14,928 4,611 191,278 46.3 Total 19,123,083 1,544,199 636,587 362,969 217,236 79,932 2,840,923 45.6 Nonresponse in the OES survey
Number of times sampled Number of OES responses Total Percentage always responding 0 1 2 3 4 5 or more 344,118 1,200,081 0 0 0 0 1,544,199 77.7 63,325 156,900 416,362 0 0 0 636,587 65.4 20,951 44,873 90,393 206,752 0 0 362,969 57.0 9,401 17,631 30,546 52,094 107,564 0 217,236 49.5 3,786 6,000 8,816 12,599 18,363 30,368 79,932 38.1 441,581 1,425,485 546,117 271,445 125,927 30,368 2,840,923 69.0 Size of establishment (number of employees) Sample units Responses Response rate (percent) Less than 5 986,296 866,805 87.89 5–9 910,993 763,726 83.83 10–19 1,040,612 828,966 79.66 20–49 1,090,078 802,078 73.58 50–99 589,566 390,217 66.19 100–249 432,045 272,761 63.13 250 or more 234,234 140,972 60.18 Total 5,283,824 4,065,525 76.94 Source: U.S. Bureau of Labor Statistics. Type of area and employment level Sample units Responses Response rate (percent) Rural 583,344 462,771 79.33 479,335 381,796 79.65 27,832 22,290 80.09 Urban 1,029,513 822,499 79.89 100,000–249,999 902,216 705,597 78.21 561,345 431,676 76.90 783,801 580,604 74.08 916,438 658,292 71.83 Total 5,283,824 4,065,525 76.94 Source: U.S. Bureau of Labor Statistics. Industry(1) Sample units Responses Response rate (percent) Agriculture, forestry, fishing and hunting (11) 26,441 22,254 84.17 Mining, quarrying, and oil and gas extraction (21) 35,421 25,021 70.64 Utilities (22) 31,097 23,070 74.19 Construction (23) 416,527 333,016 79.95 Manufacturing (31–33) 607,338 466,177 76.76 Wholesale trade (42) 404,447 302,197 74.72 Retail trade (44–45) 722,334 584,131 80.87 Transportation and warehousing (48-49) 199,623 150,713 75.50 Information (51) 161,908 114,783 70.89 Finance and insurance (52) 273,910 203,061 74.13 Real estate and rental and leasing (53) 177,584 139,780 78.71 Professional, scientific, and technical services (54) 445,644 338,456 75.95 Management of companies and enterprises (55) 57,787 38,099 65.93 Administrative and support and waste management and remediation (56) 350,736 246,877 70.39 Educational services (61) 89,970 67,521 75.05 Health care and social assistance (62) 522,261 406,286 77.79 Arts, entertainment, and recreation (71) 124,370 97,198 78.15 Accommodation and food services (72) 280,480 209,736 74.78 Other services (81) 355,946 297,149 83.48 Total 5,283,824 4,065,525 76.94 Establishments observed longitudinally in the OES data
Size of establishment (number of employees) Longitudinal sample OES data All OES data Number of establishments Number of establishment observations Sum of weighted employment Number of establishments Number of establishment observations Sum of weighted employment Less than 5 122,667 422,448 16,891,173 611,570 986,296 35,846,005 12.6 16.5 6.3 21.5 18.7 6.7 5–9 159,619 453,900 23,421,426 518,187 910,993 44,967,486 16.4 17.7 8.7 18.2 17.2 8.4 10–19 207,280 540,139 34,007,075 560,989 1,040,612 64,079,837 21.3 21.1 12.7 19.7 19.7 11.9 20–49 228,322 553,239 49,142,412 562,800 1,090,078 95,153,047 23.4 21.6 18.3 19.8 20.6 17.7 50–99 115,707 273,888 34,501,796 291,485 589,566 72,419,770 11.9 10.7 12.9 10.3 11.2 13.5 100–249 89,741 206,741 41,163,139 197,785 432,045 84,154,293 9.2 8.1 15.3 7.0 8.2 15.7 250 or more 50,521 111,840 69,057,045 98,107 234,234 140,628,219 5.2 4.4 25.7 3.5 4.4 26.2 Total 973,857 2,562,195 268,184,068 2,840,923 5,283,824 537,248,657 100.0 100.0 100.0 100.0 100.0 100.0 Source: U.S. Bureau of Labor Statistics. Type of area and employment level Longitudinal sample OES data All OES data Number of establishments Number of establishment observations Sum of weighted employment Number of establishments Number of establishment observations Sum of weighted employment Rural 209,604 551,833 37,667,214 594,488 1,090,511 71,602,980 Percent of total 21.5 21.5 14.0 20.9 20.6 13.3 79,934 303,089 16,908,045 240,183 608,305 33,199,720 8.2 11.8 6.3 8.5 11.5 6.2 112,940 237,123 19,597,654 303,771 459,730 36,351,654 11.6 9.3 7.3 10.7 8.7 6.8 16,730 11,621 1,161,515 50,534 22,476 2,051,606 1.7 0.5 0.4 1.8 0.4 0.4 Urban 764,253 2,010,362 230,516,854 2,246,435 4,193,313 465,645,675 Percent of total 78.5 78.5 86.0 79.1 79.4 86.7 171,641 555,014 29,866,137 453,602 1,066,381 55,250,347 17.6 21.7 11.1 16.0 20.2 10.3 167,941 440,145 36,287,734 485,119 890,038 69,278,776 17.2 17.2 13.5 17.1 16.8 12.9 110,588 287,261 32,103,597 310,269 592,373 62,183,364 11.4 11.2 12.0 10.9 11.2 11.6 110,791 346,776 51,978,068 352,847 774,220 107,986,734 11.4 13.5 19.4 12.4 14.7 20.1 203,292 381,166 80,281,318 644,598 870,301 170,946,454 20.9 14.9 29.9 22.7 16.5 31.8 Total 973,857 2,562,195 268,184,068 2,840,923 5,283,824 537,248,655 Percent of total 100.0 100.0 100.0 100.0 100.0 100.0 Source: U.S. Bureau of Labor Statistics. Industry(1) Longitudinal sample OES data All OES data Number of establishments Number of establishment observations Sum of weighted employment Number of establishments Number of establishment observations Sum of weighted employment Agriculture, forestry, fishing and hunting (11) 5,335 13,934 1,065,211 15,100 26,441 1,842,915 0.5 0.5 0.4 0.5 0.5 0.3 Mining, quarrying, and oil and gas extraction (21) 6,036 15,891 1,222,543 18,400 35,421 3,091,855 0.6 0.6 0.5 0.6 0.7 0.6 Utilities (22) 5,973 16,675 1,469,998 14,308 31,097 2,583,031 0.6 0.7 0.5 0.5 0.6 0.5 Construction (23) 78,852 206,549 17,210,413 233,889 416,527 33,360,389 8.1 8.1 6.4 8.2 7.9 6.2 Manufacturing (31–33) 121,725 332,598 32,723,438 283,415 607,338 60,318,059 12.5 13.0 12.2 10.0 11.5 11.2 Wholesale trade (42) 74,106 196,825 12,962,191 209,083 404,447 27,126,934 7.6 7.7 4.8 7.4 7.7 5.0 Retail trade (44–45) 132,765 346,269 40,659,870 408,653 722,334 76,580,487 13.6 13.5 15.2 14.4 13.7 14.3 Transportation and warehousing (48–49) 35,960 93,168 10,312,104 105,628 199,623 20,737,879 3.7 3.6 3.8 3.7 3.8 3.9 Information (51) 26,787 69,859 5,496,517 87,118 161,908 13,492,553 2.8 2.7 2.0 3.1 3.1 2.5 Finance and insurance (52) 46,176 119,158 12,684,963 154,576 273,910 29,337,205 4.7 4.7 4.7 5.4 5.2 5.5 Real estate and rental and leasing (53) 33,084 85,471 4,859,550 99,422 177,584 10,248,220 3.4 3.3 1.8 3.5 3.4 1.9 Professional, scientific, and technical services (54) 79,666 207,674 16,756,831 248,316 445,644 36,852,314 8.2 8.1 6.2 8.7 8.4 6.9 Management of companies and enterprises (55) 10,088 24,601 4,000,724 29,331 57,787 9,700,806 1.0 1.0 1.5 1.0 1.1 1.8 Administrative and support and waste management and remediation (56) 57,722 147,101 15,295,441 195,019 350,736 38,134,521 5.9 5.7 5.7 6.9 6.6 7.1 Educational services (61) 16,403 42,837 6,190,217 47,794 89,970 12,124,895 1.7 1.7 2.3 1.7 1.7 2.3 Health care and social assistance (62) 101,719 271,282 42,645,992 267,803 522,261 75,309,497 10.4 10.6 15.9 9.4 9.9 14.0 Arts, entertainment, and recreation (71) 23,834 63,458 4,634,386 65,579 124,370 8,966,961 2.4 2.5 1.7 2.3 2.4 1.7 Accommodation and food services (72) 44,582 113,818 27,414,611 166,211 280,480 58,409,686 4.6 4.4 10.2 5.9 5.3 10.9 Other services (81) 73,044 195,027 10,579,071 191,278 355,946 19,030,449 7.5 7.6 3.9 6.7 6.7 3.5 Total 973,857 2,562,195 268,184,071 2,840,923 5,283,824 537,248,656 100.0 100.0 100.0 100.0 100.0 100.0 Occupation(1) Longitudinal sample data All OES data Number Percent distribution Number Percent distribution Management, business, and financial occupations (11, 13) 23,374,372 8.7 50,451,962 9.4 Computer, engineering, and science occupations (15, 17, 19) 12,018,833 4.5 26,769,703 5.0 Education, legal, community service, arts, and media occupations (21, 23, 25, 27) 12,906,575 4.8 25,482,588 4.8 Healthcare practitioners and technical occupations (29) 16,250,249 6.1 29,138,257 5.4 Service occupations (31, 33, 35, 37, 39) 54,139,466 20.2 108,192,549 20.2 Sales and related occupations (41) 33,745,410 12.6 68,478,431 12.8 Office and administrative support occupations (43) 45,983,438 17.2 92,273,301 17.2 Farming, fishing, and forestry occupations (45) 1,078,612 0.4 1,950,072 0.4 Construction and extraction occupations (47) 13,372,319 5.0 26,509,977 4.9 Installation, maintenance, and repair occupations (49) 11,700,164 4.4 22,462,233 4.2 Production occupations (51) 22,898,336 8.6 43,469,506 8.1 Transportation and material moving occupations (53) 20,327,148 7.6 41,110,559 7.7 Total 267,794,922 100.0 536,289,138 100.0
Research efforts and opportunities
Matthew Dey and Elizabeth Weber Handwerker, "Longitudinal data from the Occupational Employment Statistics survey," Monthly Labor Review, U.S. Bureau of Labor Statistics, October 2016, https://doi.org/10.21916/mlr.2016.49
1 Shail Butani and Michael McElroy, “Managing various customer needs for Occupational Employment Statistics survey,” 1999 proceedings of the American Statistical Association, section on government statistics, pp. 370–73.
2 Handbook of methods, chapter 3 (U.S. Bureau of Labor Statistics), https://www.bls.gov/opub/hom/pdf/homch3.pdf.
3 For further reading, see Ernest Lawley, Marie Stetster, and Eduaras Valaitis, “Alternative allocation designs for a highly stratified establishment survey,” (U.S. Bureau of Labor Statistics), December 2007, https://www.bls.gov/osmr/research-papers/2007/pdf/st070020.pdf.
4 David Piccone and Marie C. Stetser, “National sample reallocation for the Occupational Employment Statistics survey,” 2009 proceedings of the American Statistical Association, section on government statistics, pp. 3626–38.
5 “Survey methods and reliability statement for the May 2014 Occupational Employment Statistics survey,” (U.S. Bureau of Labor Statistics), https://www.bls.gov/oes/current/methods_statement.pdf.
6 Piccone and Stetser, “National sample reallocation,” pp. 3626–38.
7 Shail Butani, Kenneth W. Robertson, and Kirk Mueller, “Assigning permanent random numbers to the Bureau of Labor Statistics longitudinal (universe) data base,” proceedings of the 1998 American Statistical Association, section on survey research methods, https://www.bls.gov/osmr/research-papers/1998/pdf/st980080.pdf.
8 Handbook of methods, chapter 3.
9 In the analyses of sampling and response rates, we include 108,550 establishments, largely from the 1999 and 2000 OES panels, which cannot be linked uniquely to the QCEW data; therefore, we cannot determine if there is any longitudinal link to establishments sampled in the OES data in other panels.
10 The May 2008 sample was reduced from the usual 200,000 establishments overall (including state and local government and establishments in Guam, Puerto Rico, and the Virgin Islands) to approximately 174,000 establishments overall because of budget constraints, as described on page 7 of Occupational employment and wages, 2008, USDL-09-0457 (U.S. Department of Labor, May 1, 2009, reissued May 29, 2009), https://www.bls.gov/news.release/archives/ocwage_05012009.pdf.
11 The sample for the November 2006 panel was accidentally drawn without using the permanent random numbers on the sample frame.
12 Polly A. Phipps and Carrie K. Jones, “Factors affecting response to the Occupational Employment Statistics survey,” 2007 proceedings of the Federal Committee on Statistical Methodology, November 2007, https://www.bls.gov/osmr/research-papers/2007/pdf/st070170.pdf.
13 Technical note to Occupational employment and wages—May 2013, USDL-14-0528 (U.S. Department of Labor, April 11, 2014), https://www.bls.gov/news.release/archives/ocwage_04012014.pdf.
14 Phipps and Jones, “Factors affecting response.” Note that some employers go out of business in the year between the sample date and the survey date.
15 Ibid.
16 Ibid.
17 Handbook of methods, chapter 3.
18 See endnote 9.
19 For details, see Handbook of methods, chapter 3.
20 We use the final benchmark weights that were created to produce estimates for November 2002, May 2005, May 2008, May 2011, and May 2014.
21 There were changes to the Standard Occupational Classification (SOC) system in 2010 that affect occupational categories. For this table, we use occupational groupings that are consistent over time, but differ slightly from the 2010 SOC categories.
22 For the 1999–May 2005 wage ranges, see Occupational employment and wages in 1999 based on the new Standard Occupational Classification system, USDL-00-368 (U.S. Department of Labor, December 20, 2000), concepts section of the technical note, https://www.bls.gov/news.release/History/ocwage_12202000.txt; for the November 2005 adjustment, see Occupational employment and wages, 2006, USDL-07-0712 (U.S. Department of Labor, May 17, 2007), p. 5, https://www.bls.gov/news.release/archives/ocwage_05172007.pdf; for the November 2008 adjustment, see Occupational employment and wages–May 2009, USDL-10-0646 (U.S. Department of Labor, May 14, 2010), p. 6, https://www.bls.gov/news.release/archives/ocwage_05142010.pdf; and for the November 2013 adjustment, see Survey methods and reliability statement for the May 2015 Occupational Employment Statistics survey, (U.S. Bureau of Labor Statistics), p. 4, https://www.bls.gov/oes/current/methods_statement.pdf.
23 Zachary Warren, “Occupational shares in growing and shrinking establishments,” Occupational employment and wages, May 2005, bulletin 2585, (U.S. Department of Labor, May 2007), pp. 6–19, https://www.bls.gov/oes/shares.pdf.
24 Dina Itkin “The effect of business ownership change on occupational employment and wages,” Monthly Labor Review, September 2008, pp. 3–23, https://www.bls.gov/opub/mlr/2008/09/art1full.pdf.
25 Dina Itkin and Laurie Salmon, “How occupational employment is affected by mass layoffs,” Monthly Labor Review, June 2011, pp. 3–33, https://www.bls.gov/opub/mlr/2011/06/art1full.pdf.
26 More information about the Visiting Researcher program is available at https://www.bls.gov/rda/home.htm.
27 More information about the ASA/NSF/BLS Research Fellow Program is available at https://www.bls.gov/osmr/asa_nsf_bls_fellowship_info.htm.
28 For a description of the CSWEP Summer Economics Fellows Program, see https://www.aeaweb.org/about-aea/committees/summer-fellows-program.